{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "qnokSenFkUk7",
        "vlXxUWCbrpWB"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot\n",
        "import numpy as np\n",
        "from matplotlib.pyplot import figure\n",
        "import matplotlib\n",
        "from statsmodels.stats.contingency_tables import SquareTable\n",
        "from scipy import stats\n",
        "\n",
        "matplotlib.rc('pdf', fonttype=42)\n",
        "\n",
        "\n",
        "P0 = pd.read_csv(\"data_clean_no_names_deidentified - P0.csv\").sort_values(\"UID\").reset_index(drop=True)\n",
        "P1 = pd.read_csv(\"data_clean_no_names_deidentified - P1.csv\").sort_values(\"UID\").reset_index(drop=True)\n",
        "P2 = pd.read_csv(\"data_clean_no_names_deidentified - P2.csv\").sort_values(\"UID\").reset_index(drop=True)\n",
        "P3 = pd.read_csv(\"data_clean_no_names_deidentified - P3.csv\").sort_values(\"UID\").reset_index(drop=True)\n",
        "P4 = pd.read_csv(\"data_clean_no_names_deidentified - P4.csv\").sort_values(\"UID\").reset_index(drop=True)"
      ],
      "metadata": {
        "id": "TDwuDT3IJx1R"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if all rows are aligned\n",
        "for aligned_uids in zip(P0.UID, P1.UID, P2.UID, P3.UID, P4.UID):\n",
        "  first = aligned_uids[0]\n",
        "  for uid in aligned_uids[1:]:\n",
        "    if first!=uid:\n",
        "      print(\"ERROR! Not sorted.\")"
      ],
      "metadata": {
        "id": "Azuq-t22k6as"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "-----\n",
        "# H1: Main figure"
      ],
      "metadata": {
        "id": "YIlBSCEZj3BI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## H1a\n",
        "There is a significant difference between the time students take to perform the data analysis replication and the time they expect to take."
      ],
      "metadata": {
        "id": "hv1ZQpDw92la"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "H1a_pre_test = P1['1. How many hours do you think it would take you to reproduce figure A?']\n",
        "H1a_post_test = P2['1. How many hours did it take you to reproduce figure A? Please consult your time tracking sheet.']"
      ],
      "metadata": {
        "id": "-VmRdjq1VdWn"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Pre-test average\", H1a_pre_test.mean())\n",
        "print(\"Post-test average\", H1a_post_test.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jg-UVGngWjJ1",
        "outputId": "4bfab372-5a4b-40e3-91c0-9c03f2334d37"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pre-test average 9.013677811550153\n",
            "Post-test average 10.531489361702128\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Pre-test median\",H1a_pre_test.median())\n",
        "print(\"Post-test median\",H1a_post_test.median())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8EtWIWYXy0N",
        "outputId": "03027c65-f0ef-4784-9216-73e53b3d64e4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pre-test median 5.0\n",
            "Post-test median 8.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QeTEbuJcTsIZ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H1a_pre_test, 50, range = [0,50], alpha=0.5, label='H1a_pre_test')\n",
        "pyplot.hist(H1a_post_test, 50, range = [0,50], alpha=0.5, label='H1a_post_test')\n",
        "pyplot.legend(loc='upper right')\n",
        "pyplot.xlabel(\"Hours\")\n",
        "pyplot.ylabel(\"# Students\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "3X-erdTcWy_B",
        "outputId": "7c6d174d-7276-4edc-9b1b-b9e39c025ccd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '# Students')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import matplotlib as mpl\n",
        "\n",
        "mpl.rcParams['axes.spines.right'] = False\n",
        "mpl.rcParams['axes.spines.top'] = False"
      ],
      "metadata": {
        "id": "w5trv-33CLia"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy\n",
        "\n",
        "H1a_pre_test_mean = H1a_pre_test.mean()\n",
        "H1a_pre_test_boostrap = scipy.stats.bootstrap((H1a_pre_test.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "\n",
        "print(H1a_pre_test_mean, H1a_pre_test_boostrap.confidence_interval.low, H1a_pre_test_boostrap.confidence_interval.high)\n",
        "\n",
        "H1a_post_test_mean = H1a_post_test.mean()\n",
        "H1a_post_test_boostrap = scipy.stats.bootstrap((H1a_post_test.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "\n",
        "print(H1a_post_test_mean, H1a_post_test_boostrap.confidence_interval.low, H1a_post_test_boostrap.confidence_interval.high)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zCP386pHBxXc",
        "outputId": "3a3aaac7-8ea8-440d-f88e-429bef19ec67"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9.013677811550153 7.968085106382978 10.74778927118379\n",
            "10.531489361702128 9.789004036745462 11.406843113787408\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(5, 3))\n",
        "\n",
        "kde_pre = stats.gaussian_kde(H1a_pre_test)\n",
        "kde_post = stats.gaussian_kde(H1a_post_test)\n",
        "xx = np.linspace(0, 50, 100)\n",
        "\n",
        "pyplot.hist(H1a_pre_test, 50, range = [0,50], alpha=0.3, label='Pre-survey')\n",
        "pyplot.hist(H1a_post_test, 50, range = [0,50], alpha=0.3, label='Post-survey')\n",
        "pyplot.legend(loc='upper left')\n",
        "pyplot.xlabel(\"Hours\")\n",
        "pyplot.ylabel(\"Number of students\")\n",
        "pyplot.plot(xx, kde_pre(xx)*len(H1a_pre_test), linewidth = 3, color=\"steelblue\")\n",
        "pyplot.plot(xx, kde_post(xx)*len(H1a_post_test), linewidth = 3, color=\"darkorange\")\n",
        "\n",
        "pyplot.ylim([0,70])\n",
        "plt.savefig(\"H1a_1.pdf\",  bbox_inches=\"tight\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "DbS5oW0lazA5",
        "outputId": "fc5c82ca-4180-4ff3-adf3-adb7b4934bf0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_rel(H1a_pre_test, H1a_post_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qp9Xup88Xk0c",
        "outputId": "bdb22e4e-cb97-4bfa-ff66-721996c169f1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TtestResult(statistic=-2.167369292496649, pvalue=0.030925860688734153, df=328)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(5, 3))\n",
        "\n",
        "kde_pre = stats.gaussian_kde(H1a_post_test - H1a_pre_test)\n",
        "kde_post = stats.gaussian_kde(H1a_post_test - H1a_pre_test)\n",
        "xx = np.linspace(-20, 20, 80)\n",
        "\n",
        "pyplot.hist(H1a_post_test - H1a_pre_test, range = [-20,20], bins = 41, alpha = 0.3,color = 'tab:green', label='H1a_post_test - H1a_pre_test')\n",
        "\n",
        "#pyplot.legend(loc='upper right')\n",
        "pyplot.xlabel(\"Hours\")\n",
        "pyplot.ylabel(\"Number of students\")\n",
        "pyplot.plot(xx, kde_pre(xx)*len(H1a_post_test - H1a_pre_test),linewidth = 3, color=\"tab:green\")\n",
        "pyplot.ylim([0,70])\n",
        "pyplot.vlines(0,0,70, linestyle='--')\n",
        "plt.savefig(\"H1a_2.pdf\",  bbox_inches=\"tight\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "CErf4P2k9YtX",
        "outputId": "464900ec-b86e-4958-a9df-dfabcf6e241b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(H1a_post_test < H1a_pre_test) / len(H1a_post_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SjirsYwJAJGy",
        "outputId": "7b115b81-dd3b-4fdf-bffb-0f308cc57d56"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3069908814589666"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1,3, figsize=(10, 3))\n",
        "\n",
        "for j in range(3):\n",
        "  sbplt = ax[j]\n",
        "  for i in range(len(H1a_post_test)):\n",
        "    if H1a_pre_test[i]>50 or H1a_post_test[i]>50:\n",
        "      continue\n",
        "    else:\n",
        "      if j==0:\n",
        "        sbplt.set_title('Students who took less\\n hours than expected')\n",
        "        color = 'tab:blue'\n",
        "        if H1a_pre_test[i] > H1a_post_test[i]:\n",
        "          sbplt.plot([0,1], [H1a_pre_test[i], H1a_post_test[i]], marker = 'o', color = color, alpha = 0.1)\n",
        "      elif j==1:\n",
        "        sbplt.set_title('Students who took as \\n many hours as expected')\n",
        "        color = 'black'\n",
        "        if H1a_pre_test[i] == H1a_post_test[i]:\n",
        "          sbplt.plot([0,1], [H1a_pre_test[i], H1a_post_test[i]], marker = 'o', color = color, alpha = 0.1)\n",
        "      elif j==2:\n",
        "        sbplt.set_title('Students who took more\\n hours than expected')\n",
        "        color = 'tab:orange'\n",
        "        if H1a_pre_test[i] < H1a_post_test[i]:\n",
        "          sbplt.plot([0,1], [H1a_pre_test[i], H1a_post_test[i]], marker = 'o', color = color, alpha = 0.1)\n",
        "\n",
        "\n",
        "    sbplt.set_xlim([-0.2,1.2])\n",
        "\n",
        "    sbplt.set_ylim([-5,55])\n",
        "\n",
        "    #plt.yscale('log')\n",
        "    if j==0:\n",
        "      sbplt.set_ylabel('Number of hours')\n",
        "    sbplt.set_xticks([0,1])\n",
        "    sbplt.set_xticklabels(['Pre-survey', 'Post-survey'])\n",
        "\n",
        "plt.savefig(\"H1a_3.pdf\",  bbox_inches=\"tight\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "c8i-gqHBBHAc",
        "outputId": "0bbd8a5f-0d40-424d-b171-cababe70acaf"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary:** *There is a significant difference between the time students take to perform the data analysis replication and the time they expect to take.*\n",
        "\n",
        "YES. Statistically significant.\n",
        "It takes more time than expected."
      ],
      "metadata": {
        "id": "D8G6PZDP6p-Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "------\n",
        "## H1b\n",
        "There is a significant difference between how challenging performing data analysis replication tasks is and how challenging students expect it to be."
      ],
      "metadata": {
        "id": "9pwl1Z_djzfZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "H1b_pre_test = P1['3. How challenging do you expect it to be to reproduce figure A? '].apply(lambda r: int(r[0]))\n",
        "H1b_post_test = P2['3.  How challenging was it to reproduce figure A?'].apply(lambda r: int(r[0]))"
      ],
      "metadata": {
        "id": "MgFFoMhub-CX"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Pre-test average\", H1b_pre_test.mean())\n",
        "print(\"Post-test average\", H1b_post_test.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3E9SYFTijWYs",
        "outputId": "b0a6ec01-c693-4cd2-9d00-68717fc42423"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pre-test average 3.386018237082067\n",
            "Post-test average 3.1094224924012157\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Pre-test median\", H1b_pre_test.median())\n",
        "print(\"Post-test median\", H1b_post_test.median())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZ6UT049jcPB",
        "outputId": "dcaf922b-4bd1-44c9-89e9-b131ece965d3"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pre-test median 4.0\n",
            "Post-test median 3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy\n",
        "\n",
        "H1b_pre_test_mean = H1b_pre_test.mean()\n",
        "H1b_pre_test_boostrap = scipy.stats.bootstrap((H1b_pre_test.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "\n",
        "print(H1b_pre_test_mean, H1b_pre_test_boostrap.confidence_interval.low, H1b_pre_test_boostrap.confidence_interval.high)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2JFb1_cTtnRg",
        "outputId": "4392977c-65e4-4e56-a797-e4c2c72153ef"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.386018237082067 3.282674772036474 3.486322188449848\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy\n",
        "\n",
        "H1b_post_test_mean = H1b_post_test.mean()\n",
        "H1b_post_test_boostrap = scipy.stats.bootstrap((H1b_post_test.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "\n",
        "print(H1b_post_test_mean, H1b_post_test_boostrap.confidence_interval.low, H1b_post_test_boostrap.confidence_interval.high)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJdBxeolvFmc",
        "outputId": "aa941ed6-0f2b-4171-c12e-281c1b7d1413"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.1094224924012157 2.9969604863221884 3.221884498480243\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(5, 3))\n",
        "\n",
        "kde_pre = stats.gaussian_kde(H1b_pre_test)\n",
        "kde_post = stats.gaussian_kde(H1b_post_test)\n",
        "xx = np.linspace(1, 6, 20)\n",
        "\n",
        "pyplot.hist(H1b_pre_test, 5, range = [1,6], alpha=0.3, label='Pre-survey')\n",
        "pyplot.hist(H1b_post_test, 5, range = [1,6], alpha=0.3, label='Post-survey')\n",
        "pyplot.legend(loc='upper left')\n",
        "\n",
        "\n",
        "plt.axvline(x=H1b_pre_test_mean, color=\"#1f77b4\", alpha=0.5, linestyle=\"--\")\n",
        "plt.axvline(x=H1b_post_test_mean, color=\"#ff7f0e\", alpha=0.5, linestyle=\"--\")\n",
        "\n",
        "ax.axvspan(H1b_pre_test_boostrap.confidence_interval.low, H1b_pre_test_mean, alpha=0.1, color='#1f77b4')\n",
        "ax.axvspan(H1b_pre_test_boostrap.confidence_interval.high, H1b_pre_test_mean, alpha=0.1, color='#1f77b4')\n",
        "\n",
        "ax.axvspan(H1b_post_test_boostrap.confidence_interval.low, H1b_post_test_mean, alpha=0.1, color='#ff7f0e')\n",
        "ax.axvspan(H1b_post_test_boostrap.confidence_interval.high, H1b_post_test_mean, alpha=0.1, color='#ff7f0e')\n",
        "\n",
        "pyplot.ylim([0,180])\n",
        "pyplot.xlabel(\"Score\")\n",
        "pyplot.ylabel(\"Number of students\")\n",
        "\n",
        "plt.savefig(\"H1b_1.pdf\",  bbox_inches=\"tight\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "9DV2uJHTi2S0",
        "outputId": "07f46f36-c42f-452d-b435-14a8273f6798"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy\n",
        "\n",
        "dist = H1b_post_test - H1b_pre_test\n",
        "dist_mean = dist.mean()\n",
        "dist_boostrap = scipy.stats.bootstrap((dist.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "\n",
        "print(dist_mean, dist_boostrap.confidence_interval.low, dist_boostrap.confidence_interval.high)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJow7ruPAJh4",
        "outputId": "13483d09-7361-49f6-d5a3-4cf7ac1d725f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.2765957446808511 -0.4072948328267477 -0.1458966565349544\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(5, 3))\n",
        "\n",
        "kde_pre = stats.gaussian_kde(H1b_post_test - H1b_pre_test)\n",
        "kde_post = stats.gaussian_kde(H1b_post_test - H1b_pre_test)\n",
        "xx = np.linspace(-6, 6, 30)\n",
        "\n",
        "\n",
        "pyplot.hist(H1b_post_test - H1b_pre_test, 11, color = 'tab:green',range = [-6,6], alpha=0.3, label='H1b_post_test - H1b_pre_test')\n",
        "#pyplot.legend(loc='upper left')\n",
        "pyplot.xlabel(\"Score\")\n",
        "pyplot.ylabel(\"Number of students\")\n",
        "#pyplot.plot(xx, kde_pre(xx)*len(H1a_post_test - H1a_pre_test),linewidth = 3, color=\"tab:green\")\n",
        "pyplot.ylim([0,180])\n",
        "# pyplot.vlines(0,0,180, linestyle='--', )\n",
        "plt.axvline(x=0, color=\"black\", alpha=0.2, linestyle=\"-\")\n",
        "\n",
        "plt.axvline(x=dist_mean, color=\"#2ca02c\", alpha=0.5, linestyle=\"--\")\n",
        "\n",
        "ax.axvspan(dist_boostrap.confidence_interval.low, dist_mean, alpha=0.1, color='#2ca02c')\n",
        "ax.axvspan(dist_boostrap.confidence_interval.high, dist_mean, alpha=0.1, color='#2ca02c')\n",
        "\n",
        "\n",
        "plt.savefig(\"H1b_2.pdf\",  bbox_inches=\"tight\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "nKJOHFlfAfAm",
        "outputId": "d7877623-4f7b-4755-89f0-2915a0c40549"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_rel(H1b_pre_test, H1b_post_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dREhw88ZhxLN",
        "outputId": "b0491f25-193c-46cf-8dfd-41be65603f26"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TtestResult(statistic=4.182998296374833, pvalue=3.6971159067141934e-05, df=328)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary:** *There is a significant difference between how challenging performing data analysis replication tasks is and how challenging students expect it to be.*\n",
        "\n",
        "YES. Statistically significant. It is LESS challenging than expected."
      ],
      "metadata": {
        "id": "KsXb7SFn7PLU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "-----\n",
        "## H1c\n",
        "There are discrepancies between the predicted and the true distribution of time spent on the three core activities: data wrangling, data analysis, and interpretation.\n"
      ],
      "metadata": {
        "id": "qnokSenFkUk7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "A_pre = P1['2. While attempting to reproduce figure A, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [A]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "B_pre = P1['2. While attempting to reproduce figure A, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [B]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "C_pre = P1['2. While attempting to reproduce figure A, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [C]'].apply(lambda r: r.split(\" \")[0][0])"
      ],
      "metadata": {
        "id": "HgGmwuqUiTBX"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "H1c_pre_test = pd.DataFrame()\n",
        "H1c_pre_test['A']=A_pre\n",
        "H1c_pre_test['B']=B_pre\n",
        "H1c_pre_test['C']=C_pre\n",
        "H1c_pre_test = (H1c_pre_test['A']+H1c_pre_test['B']+H1c_pre_test['C'])\n",
        "H1c_pre_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01WIYShJkbUy",
        "outputId": "d6f646b3-4732-41b5-ba26-753b97fb8962"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FST    150\n",
              "SFT     89\n",
              "FTS     51\n",
              "TFS     23\n",
              "TSF      8\n",
              "STF      8\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "A_post = P2['2. While attempting to reproduce figure A, you spent some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you spent on them. More info on each activity is given below. [A]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "B_post = P2['2. While attempting to reproduce figure A, you spent some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you spent on them. More info on each activity is given below. [B]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "C_post = P2['2. While attempting to reproduce figure A, you spent some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you spent on them. More info on each activity is given below. [C]'].apply(lambda r: r.split(\" \")[0][0])"
      ],
      "metadata": {
        "id": "YB0Mr8A3mpE1"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "H1c_post_test = pd.DataFrame()\n",
        "H1c_post_test['A']=A_post\n",
        "H1c_post_test['B']=B_post\n",
        "H1c_post_test['C']=C_post\n",
        "H1c_post_test = (H1c_post_test['A']+H1c_post_test['B']+H1c_post_test['C'])\n",
        "H1c_post_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uRBO5q5Fk7hC",
        "outputId": "a127bc7a-b7cc-4d23-9bbb-af5fc441defe"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TFS    90\n",
              "SFT    79\n",
              "TSF    54\n",
              "FST    53\n",
              "FTS    27\n",
              "STF    26\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(H1c_pre_test.value_counts())\n",
        "print(H1c_post_test.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rA7CIiXCnZRo",
        "outputId": "faf6eba2-c780-4e31-9e18-1c7ed7b6d593"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FST    150\n",
            "SFT     89\n",
            "FTS     51\n",
            "TFS     23\n",
            "TSF      8\n",
            "STF      8\n",
            "Name: count, dtype: int64\n",
            "TFS    90\n",
            "SFT    79\n",
            "TSF    54\n",
            "FST    53\n",
            "FTS    27\n",
            "STF    26\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pd.crosstab(H1c_pre_test, H1c_post_test).values)\n",
        "print(SquareTable.homogeneity(SquareTable((pd.crosstab(H1c_pre_test,H1c_post_test).values)), method=\"stuart_maxwell\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MM5XSk3qnBxl",
        "outputId": "0b8bde56-9958-4deb-aece-713c62564ecc"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[30 13 33  7 43 24]\n",
            " [10  5  8  8  9 11]\n",
            " [ 9  3 31  8 26 12]\n",
            " [ 1  3  1  0  2  1]\n",
            " [ 3  2  5  1  8  4]\n",
            " [ 0  1  1  2  2  2]]\n",
            "df          5\n",
            "pvalue      0.0\n",
            "statistic   117.11642245832316\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(SquareTable.homogeneity(SquareTable((pd.crosstab(H1c_pre_test,H1c_post_test)\\\n",
        "                                           .values)), method=\"stuart_maxwell\").pvalue)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oHRTxA_rkK59",
        "outputId": "d91dbd3c-6158-4eeb-a819-c1ea138cb6cc"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.float_info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tagVLClTkk5a",
        "outputId": "4f2b6d96-ec4d-475b-c826-83a6948a4f37"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx = {\"F\": 1, \"S\": 2, \"T\": 3}\n",
        "print(\"W was\", A_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"A was\", B_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"E was\", C_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"-----\")\n",
        "print(\"W is\", A_post.apply(lambda r: idx[r]).mean())\n",
        "print(\"A is\", B_post.apply(lambda r: idx[r]).mean())\n",
        "print(\"E is\", C_post.apply(lambda r: idx[r]).mean())\n",
        "print(\"-----\")\n",
        "print(\"W diff\", A_post.apply(lambda r: idx[r]).mean()-A_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"A diff\", B_post.apply(lambda r: idx[r]).mean()-B_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"E diff\", C_post.apply(lambda r: idx[r]).mean()-C_pre.apply(lambda r: idx[r]).mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eM1ZaY02pcdF",
        "outputId": "a71d8b76-05ad-43be-8378-09910153b75b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "W was 1.4832826747720365\n",
            "A was 1.8389057750759878\n",
            "E was 2.6778115501519757\n",
            "-----\n",
            "W is 2.1945288753799392\n",
            "A is 1.6474164133738602\n",
            "E is 2.1580547112462005\n",
            "-----\n",
            "W diff 0.7112462006079028\n",
            "A diff -0.1914893617021276\n",
            "E diff -0.5197568389057752\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(stats.ttest_rel(A_pre.apply(lambda r: idx[r]), A_post.apply(lambda r: idx[r])))\n",
        "print(stats.ttest_rel(B_pre.apply(lambda r: idx[r]), B_post.apply(lambda r: idx[r])))\n",
        "print(stats.ttest_rel(C_pre.apply(lambda r: idx[r]), C_post.apply(lambda r: idx[r])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Th_BmKVpl2u",
        "outputId": "ef43b3fb-b5e2-49ae-bd2d-32135243ee16"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TtestResult(statistic=-12.90933401475317, pvalue=4.127343780743964e-31, df=328)\n",
            "TtestResult(statistic=3.779981561110528, pvalue=0.00018628782983987055, df=328)\n",
            "TtestResult(statistic=10.388562146538275, pvalue=4.8210509437582e-22, df=328)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary:** *There are discrepancies between the predicted and the true distribution of time spent on the three core activities: data wrangling, data analysis, and interpretation.*\n",
        "\n",
        "YES. Statistically significant. Data wrangling takes less than expected: it moves from first (more time expected) to last position (less time spent)."
      ],
      "metadata": {
        "id": "yvgkOEK_7lJ1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "-------\n",
        "## H1d\n",
        "There are discrepancies between predicted and true outcomes of the replication."
      ],
      "metadata": {
        "id": "vlXxUWCbrpWB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "H1d_a_pre_all = P1['a) The analysis will replicate exactly (the replication attempt will produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures) ']\n",
        "H1d_b_pre_all = P1['b) The analysis will replicate qualitatively (the replication attempt will produce results that have small differences with the paper, but these will still agree with the abstract-level findings of the paper)']\n",
        "H1d_c_pre_all = P1['c) The analysis won’t  replicate at all (the replication attempt will produce results that are in conflict with the abstract-level findings of the paper) ']"
      ],
      "metadata": {
        "id": "PAqTnuTFr4hg"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Students that added values that do not sum up to 100 must be discarded\n",
        "P1[\"H1d_sum\"] = (H1d_a_pre_all+H1d_b_pre_all+H1d_c_pre_all)\n",
        "students_with_mistakes = set(P1[P1['H1d_sum']!=100]['UID'])\n",
        "students_with_mistakes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAhdLazxxfUf",
        "outputId": "b5458d4b-4821-4263-faf1-9661cdbd296b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0dde5c282aeb0a2ec9814c628b0a43a5',\n",
              " '2a4482ec5a5be72a30fa0ba26df2bd06',\n",
              " 'c342a62e492d906a1dc84512e3e0cac2'}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "P1_filtered = P1[P1.UID.apply(lambda r: r not in students_with_mistakes)]\n",
        "P1_filtered = P1_filtered.sort_values(\"UID\") #just in case\n",
        "H1d_a_pre = P1_filtered['a) The analysis will replicate exactly (the replication attempt will produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures) ']\n",
        "H1d_b_pre = P1_filtered['b) The analysis will replicate qualitatively (the replication attempt will produce results that have small differences with the paper, but these will still agree with the abstract-level findings of the paper)']\n",
        "H1d_c_pre = P1_filtered['c) The analysis won’t  replicate at all (the replication attempt will produce results that are in conflict with the abstract-level findings of the paper) ']\n",
        "(H1d_a_pre+H1d_b_pre+H1d_c_pre).value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-J7KeqszYDg",
        "outputId": "68ffc046-9e8c-4cac-e3ac-1d4c7dd68d39"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100.0    326\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy\n",
        "\n",
        "H1d_a_pre_mean = H1d_a_pre.mean()\n",
        "H1d_a_pre_boostrap = scipy.stats.bootstrap((H1d_a_pre.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "print(H1d_a_pre_mean, H1d_a_pre_boostrap.confidence_interval.low, H1d_a_pre_boostrap.confidence_interval.high)\n",
        "\n",
        "\n",
        "H1d_b_pre_mean = H1d_b_pre.mean()\n",
        "H1d_b_pre_boostrap = scipy.stats.bootstrap((H1d_b_pre.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "print(H1d_b_pre_mean, H1d_b_pre_boostrap.confidence_interval.low, H1d_b_pre_boostrap.confidence_interval.high)\n",
        "\n",
        "\n",
        "H1d_c_pre_mean = H1d_c_pre.mean()\n",
        "H1d_c_pre_boostrap = scipy.stats.bootstrap((H1d_c_pre.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "print(H1d_c_pre_mean, H1d_c_pre_boostrap.confidence_interval.low, H1d_c_pre_boostrap.confidence_interval.high)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3v78Zr4J2aKj",
        "outputId": "1b047bdb-3e51-4c17-cdc7-79a6aec0635f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30.529141104294478 27.83408715885616 33.38188857101606\n",
            "57.68098159509202 55.078691086958216 60.087303030998946\n",
            "11.789877300613497 10.723351723647253 13.027794496418595\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print(plt.rcParams['axes.prop_cycle'].by_key()['color'])\n"
      ],
      "metadata": {
        "id": "CuK0rhbw3jvF"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(5, 3))\n",
        "\n",
        "pyplot.hist(H1d_a_pre, 10, alpha=0.3, label='Will replicate exactly', range = [0,100])\n",
        "pyplot.hist(H1d_b_pre, 10, alpha=0.3, label='Will replicate qualitatively',  range = [0,100])\n",
        "pyplot.hist(H1d_c_pre, 10,alpha=0.3, label='Will not replicate',  range = [0,100])\n",
        "# pyplot.legend(loc='upper left', framealpha=1)\n",
        "pyplot.xlabel(\"Percentage of papers\")\n",
        "pyplot.ylabel(\"Number or students\")\n",
        "\n",
        "plt.axvline(x=H1d_a_pre_mean, color=\"#1f77b4\", alpha=0.5, linestyle=\"--\")\n",
        "plt.axvline(x=H1d_b_pre_mean, color=\"#ff7f0e\", alpha=0.5, linestyle=\"--\")\n",
        "plt.axvline(x=H1d_c_pre_mean, color=\"#2ca02c\", alpha=0.5, linestyle=\"--\")\n",
        "\n",
        "ax.axvspan(H1d_a_pre_boostrap.confidence_interval.low, H1d_a_pre_mean, alpha=0.1, color='#1f77b4')\n",
        "ax.axvspan(H1d_a_pre_boostrap.confidence_interval.high, H1d_a_pre_mean, alpha=0.1, color='#1f77b4')\n",
        "\n",
        "ax.axvspan(H1d_b_pre_boostrap.confidence_interval.low, H1d_b_pre_mean, alpha=0.1, color='#ff7f0e')\n",
        "ax.axvspan(H1d_b_pre_boostrap.confidence_interval.high, H1d_b_pre_mean, alpha=0.1, color='#ff7f0e')\n",
        "\n",
        "ax.axvspan(H1d_c_pre_boostrap.confidence_interval.low, H1d_c_pre_mean, alpha=0.1, color='#2ca02c')\n",
        "ax.axvspan(H1d_c_pre_boostrap.confidence_interval.high, H1d_c_pre_mean, alpha=0.1, color='#2ca02c')\n",
        "\n",
        "ax.legend(loc='upper right', framealpha=1, bbox_to_anchor=(1.18, 1), fontsize=9)\n",
        "\n",
        "pyplot.ylim([0,250])\n",
        "plt.savefig(\"H1d_1.pdf\",  bbox_inches=\"tight\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "VomPHNBB6Htn",
        "outputId": "7dd0c9d8-ae55-4fd7-d47b-6d212be1ce5a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H1d_pre = H1d_a_pre/100*1+H1d_b_pre/100*2+H1d_c_pre/100*3\n",
        "pyplot.hist(H1d_pre, 15, alpha=0.5, label='H1d_pre')\n",
        "pyplot.xlim(1, 3)\n",
        "pyplot.xlabel(\"Score\")\n",
        "pyplot.ylabel(\"# Students\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "xkVMutll2jrr",
        "outputId": "d6729291-a218-4bd1-9746-ede33d7ff729"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '# Students')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx = {\"a\": 1, \"b\": 2, \"c\": 3}\n",
        "P2_filtered = P2[P2.UID.apply(lambda r: r not in students_with_mistakes)]\n",
        "P1_filtered = P2_filtered.sort_values(\"UID\") #just in case\n",
        "H1d_post = P2_filtered['8. Did you manage to reproduce figure A?'].apply(lambda r: idx[r[0]])\n",
        "H1d_post.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJBuJ79wuQLa",
        "outputId": "ae022bc3-0d40-4599-96ca-cf1ebc17fcb6"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8. Did you manage to reproduce figure A?\n",
              "2    236\n",
              "1     85\n",
              "3      5\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(stats.ttest_rel(H1d_pre, H1d_post))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMEBP1S73KGr",
        "outputId": "f819cd5d-a0e4-402b-ecb8-ae0b0b683c98"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TtestResult(statistic=1.7877786800962296, pvalue=0.07474330529544881, df=325)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Pre-test average\", H1d_pre.mean())\n",
        "print(\"Post-test average\", H1d_post.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EVGv-Msq5Wg9",
        "outputId": "6a0d25d7-65fd-4d23-d907-752469a3de6e"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pre-test average 1.8126073619631904\n",
            "Post-test average 1.7546012269938651\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H1d_pre, 15, alpha=0.5, label='H1d_pre')\n",
        "pyplot.hist(H1d_post, 15, alpha=0.5, label='H1d_post')\n",
        "pyplot.legend(loc='upper left')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "BywgGGhL7Ao_",
        "outputId": "2ad00438-cc78-4834-fdb8-08a888b1eaf3"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7e43d225a650>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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QoKCgIE2aNEmvv/763fUEAAB4jLt6Doqr8BwUwA3wHBQAd4Hv4gEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYp84BZefOnRo1apRiYmLk5eWl9evXOy2fPHmyvLy8nKZhw4Y5tbl48aImTJig4OBghYaGKjk5WaWlpXfVEQAA4DnqHFDKysrUq1cvpaen37TNsGHDdO7cOce0evVqp+UTJkzQ4cOHtWXLFm3YsEE7d+7Uc889V/fqAQCAR/Kt6wrDhw/X8OHDb9nG399fUVFRtS7Lzc3Vpk2btG/fPvXr10+S9Lvf/U4jRozQ22+/rZiYmLqWBAAAPEyjXIOSmZmpiIgIde7cWdOmTdOFCxccy7KyshQaGuoIJ5KUmJgob29v7dmzpzHKAQAAbqbOZ1BuZ9iwYXr66acVFxenvLw8/du//ZuGDx+urKws+fj4qKCgQBEREc5F+PoqLCxMBQUFtW6zoqJCFRUVjtclJSUNXTYAALBIgweUcePGOf7do0cP9ezZUw8++KAyMzM1ZMiQem0zLS1N8+fPb6gSAQCA5Rr9NuP27durbdu2On78uCQpKipK58+fd2pz9epVXbx48abXrcyePVvFxcWO6fTp041dNgAAcKFGDyhnzpzRhQsXFB0dLUlKSEhQUVGRsrOzHW22bdummpoaxcfH17oNf39/BQcHO00AAMBz1fkjntLSUsfZEEnKz89XTk6OwsLCFBYWpvnz52vMmDGKiopSXl6eZs2apQ4dOigpKUmS1LVrVw0bNkxTp07V0qVLVVVVpdTUVI0bN447eAAAgKR6nEH54osv1KdPH/Xp00eSNGPGDPXp00dz5syRj4+PvvrqK/3DP/yDOnXqpOTkZPXt21f/8z//I39/f8c2PvjgA3Xp0kVDhgzRiBEjNGDAAL333nsN1ysAAODWvIwxxtVF1FVJSYlCQkJUXFzMxz2ArbanuWa/g2e7Zr8AGhTfxQMAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYp84BZefOnRo1apRiYmLk5eWl9evXOy03xmjOnDmKjo5WYGCgEhMTdezYMac2Fy9e1IQJExQcHKzQ0FAlJyertLT0rjoCAAA8R50DSllZmXr16qX09PRaly9cuFCLFy/W0qVLtWfPHgUFBSkpKUnl5eWONhMmTNDhw4e1ZcsWbdiwQTt37tRzzz1X/14AAACP4mWMMfVe2ctL69at0+jRoyX9cPYkJiZGv/zlL/Xyyy9LkoqLixUZGamVK1dq3Lhxys3NVbdu3bRv3z7169dPkrRp0yaNGDFCZ86cUUxMzG33W1JSopCQEBUXFys4OLi+5QNoTNvTXLPfwbNds18ADapBr0HJz89XQUGBEhMTHfNCQkIUHx+vrKwsSVJWVpZCQ0Md4USSEhMT5e3trT179tS63YqKCpWUlDhNAADAczVoQCkoKJAkRUZGOs2PjIx0LCsoKFBERITTcl9fX4WFhTnaXC8tLU0hISGOKTY2tiHLBgAAlnGLu3hmz56t4uJix3T69GlXlwQAABpRgwaUqKgoSVJhYaHT/MLCQseyqKgonT9/3mn51atXdfHiRUeb6/n7+ys4ONhpAgAAnqtBA0pcXJyioqKUkZHhmFdSUqI9e/YoISFBkpSQkKCioiJlZ2c72mzbtk01NTWKj49vyHIAAICb8q3rCqWlpTp+/LjjdX5+vnJychQWFqb7779f06dP15tvvqmOHTsqLi5Or732mmJiYhx3+nTt2lXDhg3T1KlTtXTpUlVVVSk1NVXjxo27ozt4AACA56tzQPniiy80ePBgx+sZM2ZIkiZNmqSVK1dq1qxZKisr03PPPaeioiINGDBAmzZtUkBAgGOdDz74QKmpqRoyZIi8vb01ZswYLV68uAG6AwAAPMFdPQfFVXgOCuAGeA4KgLvgFnfxAACA5oWAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdX1cXYKXtaa7Z7+DZrtkvAACW4QwKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB1fVxcAwPUWbfmmwbf58KkLtc5PaN+mwfcFwPMQUAA0qay/1x5cGsruq/8Xtl56slOj7gtA4+EjHgAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6zR4QJk3b568vLycpi5dujiWl5eXKyUlRW3atFGrVq00ZswYFRYWNnQZAADAjTXKGZTu3bvr3Llzjulvf/ubY9lLL72kv/zlL/roo4+0Y8cOnT17Vk8//XRjlAEAANxUozyozdfXV1FRUTfMLy4u1rJly/Thhx/qiSeekCStWLFCXbt21e7du/Xwww83RjkAAMDNNMoZlGPHjikmJkbt27fXhAkTdOrUKUlSdna2qqqqlJiY6GjbpUsX3X///crKyrrp9ioqKlRSUuI0AQAAz9XgASU+Pl4rV67Upk2btGTJEuXn52vgwIG6dOmSCgoK5Ofnp9DQUKd1IiMjVVBQcNNtpqWlKSQkxDHFxsY2dNkAAMAiDf4Rz/Dhwx3/7tmzp+Lj49WuXTutXbtWgYGB9drm7NmzNWPGDMfrkpISQgoAAB6s0W8zDg0NVadOnXT8+HFFRUWpsrJSRUVFTm0KCwtrvWblGn9/fwUHBztNAADAczV6QCktLVVeXp6io6PVt29ftWjRQhkZGY7lR48e1alTp5SQkNDYpQAAADfR4B/xvPzyyxo1apTatWuns2fPau7cufLx8dH48eMVEhKi5ORkzZgxQ2FhYQoODtYvfvELJSQkcAcPAABwaPCAcubMGY0fP14XLlxQeHi4BgwYoN27dys8PFyStGjRInl7e2vMmDGqqKhQUlKS3n333YYuAwAAuLEGDyhr1qy55fKAgAClp6crPT29oXcNAAA8BN/FAwAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgnQa/zRhAw1i05RtXlwAALsMZFAAAYB3OoAB1wFkNAGganEEBAADWIaAAAADrEFAAAIB1uAYFgMdqymuGXnqyU5PtC2gOOIMCAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsI6vqwsAAE+waMs3Tbavl57s1GT7AlyFMygAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0edQ+315SPGAcANA3OoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA538dQi6+8XmmxfCe3bNNm+AABwF5xBAQAA1iGgAAAA6/ARTzPSlA80e+nJTk22LwCA5+EMCgAAsA4BBQAAWIePeFzsx3cM7b7Kd8oAACBxBgUAAFiIgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDrcZoxG0ZRPrQUAeB7OoAAAAOtwBgUAAFttT3PdvgfPdt2+xRkUAABgIQIKAACwjksDSnp6uh544AEFBAQoPj5ee/fudWU5AADAEi4LKH/60580Y8YMzZ07V/v371evXr2UlJSk8+fPu6okAABgCZcFlN/85jeaOnWqpkyZom7dumnp0qVq2bKlli9f7qqSAACAJVxyF09lZaWys7M1e/b/XSHs7e2txMREZWVl3dC+oqJCFRUVjtfFxcWSpJKSkkapr+xKxe0bNYLyslKX7BdoDBxHjSdt/f4m21fKEx2abF/p24432b6a0l39DMvKG66Qumqkv7GS1Lp1a3l5ed2yjUsCynfffafq6mpFRkY6zY+MjNTXX399Q/u0tDTNnz//hvmxsbGNVqNr/D9XFwB4AI6jhvRvri7AA7jvz/D1RttycXGxgoODb9nGLZ6DMnv2bM2YMcPxuqamRhcvXlSbNm1um8DqqqSkRLGxsTp9+vRtf3juiP65P0/vI/1zf57eR0/vn9T4fWzduvVt27gkoLRt21Y+Pj4qLCx0ml9YWKioqKgb2vv7+8vf399pXmhoaGOWqODgYI/9xZPonyfw9D7SP/fn6X309P5Jru2jSy6S9fPzU9++fZWRkeGYV1NTo4yMDCUkJLiiJAAAYBGXfcQzY8YMTZo0Sf369VP//v31zjvvqKysTFOmTHFVSQAAwBIuCyhjx47Vt99+qzlz5qigoEC9e/fWpk2bbrhwtqn5+/tr7ty5N3yk5Cnon/vz9D7SP/fn6X309P5JdvTRyxhjXLZ3AACAWvBdPAAAwDoEFAAAYB0CCgAAsA4BBQAAWMejA8rOnTs1atQoxcTEyMvLS+vXr7/tOpmZmXrooYfk7++vDh06aOXKlTe0SU9P1wMPPKCAgADFx8dr7969DV/8Hahr/z755BM9+eSTCg8PV3BwsBISErR582anNvPmzZOXl5fT1KVLl0bsxa3VtY+ZmZk31O/l5aWCggKndu46hpMnT661f927d3e0sWkM09LS9NOf/lStW7dWRESERo8eraNHj952vY8++khdunRRQECAevTooc8++8xpuTFGc+bMUXR0tAIDA5WYmKhjx441Vjduqj79+/3vf6+BAwfqnnvu0T333KPExMQbfv9qG+dhw4Y1Zlduqj59XLly5Q31BwQEOLVx5zEcNGhQrcfhyJEjHW1sGcMlS5aoZ8+ejgeuJSQkaOPGjbdcx5bjz6MDSllZmXr16qX09PQ7ap+fn6+RI0dq8ODBysnJ0fTp0/Xss886/RH/05/+pBkzZmju3Lnav3+/evXqpaSkJJ0/f76xunFTde3fzp079eSTT+qzzz5Tdna2Bg8erFGjRunAgQNO7bp3765z5845pr/97W+NUf4dqWsfrzl69KhTHyIiIhzL3HkMf/vb3zr16/Tp0woLC9M//dM/ObWzZQx37NihlJQU7d69W1u2bFFVVZWGDh2qsrKym67z+eefa/z48UpOTtaBAwc0evRojR49WocOHXK0WbhwoRYvXqylS5dqz549CgoKUlJSksrLm/aL1erTv8zMTI0fP17bt29XVlaWYmNjNXToUP3v//6vU7thw4Y5jeHq1asbuzu1qk8fpR+eQPrj+k+ePOm03J3H8JNPPnHq26FDh+Tj43PDcWjDGN53331asGCBsrOz9cUXX+iJJ57QU089pcOHD9fa3qrjzzQTksy6detu2WbWrFmme/fuTvPGjh1rkpKSHK/79+9vUlJSHK+rq6tNTEyMSUtLa9B66+pO+lebbt26mfnz5ztez5071/Tq1avhCmtAd9LH7du3G0nm+++/v2kbTxrDdevWGS8vL3PixAnHPJvH8Pz580aS2bFjx03bPPPMM2bkyJFO8+Lj482//uu/GmOMqampMVFRUeY//uM/HMuLioqMv7+/Wb16deMUfofupH/Xu3r1qmndurVZtWqVY96kSZPMU0891QgV3r076eOKFStMSEjITZd72hguWrTItG7d2pSWljrm2TyG99xzj3n//fdrXWbT8efRZ1DqKisrS4mJiU7zkpKSlJWVJUmqrKxUdna2Uxtvb28lJiY62riTmpoaXbp0SWFhYU7zjx07ppiYGLVv314TJkzQqVOnXFRh/fXu3VvR0dF68skntWvXLsd8TxvDZcuWKTExUe3atXOab+sYFhcXS9INv3M/drvjMD8/XwUFBU5tQkJCFB8f7/IxvJP+Xe/y5cuqqqq6YZ3MzExFRESoc+fOmjZtmi5cuNCgtdbXnfaxtLRU7dq1U2xs7A3v2D1tDJctW6Zx48YpKCjIab5tY1hdXa01a9aorKzspl8rY9PxR0D5kYKCghueZBsZGamSkhJduXJF3333naqrq2ttc/01Du7g7bffVmlpqZ555hnHvPj4eK1cuVKbNm3SkiVLlJ+fr4EDB+rSpUsurPTORUdHa+nSpfr444/18ccfKzY2VoMGDdL+/fslyaPG8OzZs9q4caOeffZZp/m2jmFNTY2mT5+uRx99VD/5yU9u2u5mx+G18bn2X9vG8E77d71XXnlFMTExTv/DHzZsmP7rv/5LGRkZeuutt7Rjxw4NHz5c1dXVjVH6HbvTPnbu3FnLly/Xp59+qj/+8Y+qqanRI488ojNnzkjyrDHcu3evDh06dMNxaNMYHjx4UK1atZK/v7+ef/55rVu3Tt26dau1rU3Hn8sedQ/X+vDDDzV//nx9+umnTtdnDB8+3PHvnj17Kj4+Xu3atdPatWuVnJzsilLrpHPnzurcubPj9SOPPKK8vDwtWrRIf/jDH1xYWcNbtWqVQkNDNXr0aKf5to5hSkqKDh065NJrmhpTffq3YMECrVmzRpmZmU4XkY4bN87x7x49eqhnz5568MEHlZmZqSFDhjRo3XVxp31MSEhweof+yCOPqGvXrvrP//xPvfHGG41dZr3VZwyXLVumHj16qH///k7zbRrDzp07KycnR8XFxfrzn/+sSZMmaceOHTcNKbbgDMqPREVFqbCw0GleYWGhgoODFRgYqLZt28rHx6fWNlFRUU1Z6l1Zs2aNnn32Wa1du/aGU3nXCw0NVadOnXT8+PEmqq7h9e/f31G/p4yhMUbLly/XxIkT5efnd8u2NoxhamqqNmzYoO3bt+u+++67ZdubHYfXxufaf20aw7r075q3335bCxYs0F//+lf17Nnzlm3bt2+vtm3bus0YXq9Fixbq06ePo35PGcOysjKtWbPmjoK/K8fQz89PHTp0UN++fZWWlqZevXrpt7/9ba1tbTr+CCg/kpCQoIyMDKd5W7ZscbwT8PPzU9++fZ3a1NTUKCMj46af59lm9erVmjJlilavXu10S9zNlJaWKi8vT9HR0U1QXePIyclx1O8JYyj9cOfB8ePH7+h/jK4cQ2OMUlNTtW7dOm3btk1xcXG3Xed2x2FcXJyioqKc2pSUlGjPnj1NPob16Z/0w10Qb7zxhjZt2qR+/frdtv2ZM2d04cIFtxnD61VXV+vgwYOO+j1hDKUfbsetqKjQz3/+89u2deUYXq+mpkYVFRW1LrPq+GvQS24tc+nSJXPgwAFz4MABI8n85je/MQcOHDAnT540xhjz6quvmokTJzra//3vfzctW7Y0M2fONLm5uSY9Pd34+PiYTZs2OdqsWbPG+Pv7m5UrV5ojR46Y5557zoSGhpqCggLr+/fBBx8YX19fk56ebs6dO+eYioqKHG1++ctfmszMTJOfn2927dplEhMTTdu2bc358+ebvH/G1L2PixYtMuvXrzfHjh0zBw8eNC+++KLx9vY2W7dudbRx5zG85uc//7mJj4+vdZs2jeG0adNMSEiIyczMdPqdu3z5sqPNxIkTzauvvup4vWvXLuPr62vefvttk5uba+bOnWtatGhhDh486GizYMECExoaaj799FPz1VdfmaeeesrExcWZK1euWN+/BQsWGD8/P/PnP//ZaZ1Lly4ZY374nXj55ZdNVlaWyc/PN1u3bjUPPfSQ6dixoykvL2/S/tW3j/PnzzebN282eXl5Jjs724wbN84EBASYw4cPO9q48xheM2DAADN27Ngb5ts0hq+++qrZsWOHyc/PN1999ZV59dVXjZeXl/nrX/9qjLH7+PPogHLtltPrp0mTJhljfrgN7PHHH79hnd69exs/Pz/Tvn17s2LFihu2+7vf/c7cf//9xs/Pz/Tv39/s3r278TtTi7r27/HHH79le2N+uK06Ojra+Pn5mXvvvdeMHTvWHD9+vGk79iN17eNbb71lHnzwQRMQEGDCwsLMoEGDzLZt227YrruOoTE/3NIXGBho3nvvvVq3adMY1tY3SU7H1eOPP+70O2iMMWvXrjWdOnUyfn5+pnv37ua///u/nZbX1NSY1157zURGRhp/f38zZMgQc/To0SbokbP69K9du3a1rjN37lxjjDGXL182Q4cONeHh4aZFixamXbt2ZurUqS4J0MbUr4/Tp093HF+RkZFmxIgRZv/+/U7bdecxNMaYr7/+2khy/KH/MZvG8F/+5V9Mu3btjJ+fnwkPDzdDhgxxqtnm48/LGGMa6GQMAABAg+AaFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACs8/8B61zLj1qnGRMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "pyplot.hist(H1d_post, 15, alpha=0.5, label='H1d_post')\n",
        "pyplot.close()\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(5, 3))\n",
        "\n",
        "pyplot.bar([1,2,3], [sum(H1d_post ==1), sum(H1d_post ==2), sum(H1d_post ==3)], color = ['tab:blue', 'tab:orange', 'tab:green'], alpha = 0.3)\n",
        "\n",
        "pyplot.ylabel('Number of students')\n",
        "pyplot.xticks([1,2,3], ['Replicated\\nexactly','Replicated\\nqualitatively','Did not\\nreplicate'], fontsize= 8)\n",
        "\n",
        "pyplot.ylim([0,250])\n",
        "plt.savefig(\"H1d_2.pdf\",  bbox_inches=\"tight\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "eiwcIaL6HJvT",
        "outputId": "fc4db0f5-d346-4084-b904-7409a9ea08b2"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist([np.rint(x) for x in H1d_pre], 3, alpha=0.5, label='H1d_pre')\n",
        "pyplot.hist(H1d_post, 3, alpha=0.5, label='H1d_post')\n",
        "pyplot.legend(loc='upper left')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "WaxZznRn7zsv",
        "outputId": "3ed95656-59b6-4446-c0db-8e798ca12145"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7e43d225ab30>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H1d_post - ([np.rint(x) for x in H1d_pre]), color = 'tab:green', range = [-2,2], bins = 5, alpha=0.5, label='H1d_post - H1d_pre')\n",
        "pyplot.legend(loc='upper left')\n",
        "pyplot.vlines(0,0,220,linestyle = '--')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "jZp7dThCsOOe",
        "outputId": "24c7d5b8-917b-4839-c08d-c29457cfb1b4"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.LineCollection at 0x7e4395c042b0>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary:** *There are discrepancies between predicted and true outcomes of the replication.*\n",
        "\n",
        "YES, but NOT statically significant.\n",
        "Possible reason: the papers were preselected to be (with enough effort) \"replicable\". Students were not actually exposed to randomly sampled papers."
      ],
      "metadata": {
        "id": "aoHEBY0o8nhz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "-------\n",
        "# H2 - Advanced figure\n",
        "Discrepancies between predictions and true outcomes persist as students solve replication tasks."
      ],
      "metadata": {
        "id": "lQ21WCcw9fek"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## H2a\n",
        "There is a significant difference between the time students take to perform the data analysis replication and the time they expect to take."
      ],
      "metadata": {
        "id": "XT6M83gZLlra"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We asked the same question 3 times\n",
        "H2a_pre_test_P1 = P1['1. How many hours do you think it would take you to reproduce figure B?']\n",
        "H2a_pre_test_P2 = P2['1. How many hours do you think it would take you to reproduce figure B?']\n",
        "H2a_pre_test_P3 = P3['1. How many hours do you think it would take you to reproduce figure B?']\n",
        "H2a_post_test = P4['1. How many hours did it take you to reproduce figure B? Please consult your time tracking sheet.']"
      ],
      "metadata": {
        "id": "ZENLEjFT9BM2"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H2a_pre_test_P1, 25, alpha=0.3, label='Pre P1', range=(0,50))\n",
        "pyplot.hist(H2a_pre_test_P2, 25, alpha=0.3, label='Pre P2', range=(0,50))\n",
        "pyplot.hist(H2a_pre_test_P3, 25, alpha=0.3, label='Pre P3', range=(0,50))\n",
        "pyplot.hist(H2a_post_test, 25, alpha=0.3, label='Post', range=(0,50))\n",
        "pyplot.legend(loc='upper right')\n",
        "stats.ttest_rel(H2a_pre_test_P2, H2a_pre_test_P3)\n",
        "pyplot.xlabel(\"Hours\")\n",
        "pyplot.ylabel(\"# Students\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "RLRVL2fkNDGz",
        "outputId": "1337d0ad-a4d6-4d6b-ae55-4f7a13cfd1d2"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '# Students')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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0Hh84cKBycnL07bff6vXXX9fIkSOVlZWlli1bmvNhf8Frgszq1atVUlKisWPHSpKKiork5+ensLAwl/MiIyNVVFR03jqzZs3SjBkz6rBTAAC8y8CBAzV//nz5+fkpKirqrLuVgoKCXF6fPn1acXFxWrp06Vm1fnm15JdWrFihrl27KiIi4qzf0T+/V/v27dW+fXv169dPHTp00KJFi5Senu7+B6sFrwkyixYtUlJSkqKioi6pTnp6utLS0pyvHQ6HoqOjL7U9AAC81s/hobZ69eqlFStWqGXLlgoJCXHrvaKjo3XllVfW+vyamhqXKyVm84rbrw8fPqwPP/xQf/zjH5377Ha7Kisrnbd0/ay4uFh2u/28tfz9/RUSEuKyAQCAfxs9erSaN2+u5ORkbdu2Tfn5+crMzNSDDz6oo0ePXlTNsrIyPf7449q1a5cOHz6s7Oxs3XXXXTp27JjzJp664BVBZvHixWrZsqVuvvlm5764uDj5+voqIyPDuS83N1dHjhxRfHy8J9oEAKBBaNKkibZu3aqYmBiNGDFCXbp00d13363y8vKLngDw8fHRl19+qZSUFHXs2FHDhg3TyZMntW3bNnXr1s3kT/BvNsMwjDqrXgs1NTWKjY3V7bffrtmzZ7scGz9+vNavX68lS5YoJCTEuQJ7x44dta7vcDgUGhqq0tJSr52dMfNLIxeZ9KWRE/sPN6UOAFhBeXm58vPzFRsbq4CAAE+3c9kwY9w9vkbmww8/1JEjR3TXXXeddeyll15So0aNlJKSooqKCiUmJuq1117zQJcAAMAbeTzIDBkyROebFAoICNC8efM0b968eu4KAABYgVeskQEAALgYBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZHr/9GuZqkfOFOYV4IB4AwAKYkQEAAJbFjAwAABdg5tfI1EbTQQPdOn/s2LF68803JUm+vr6KiYnRnXfeqccff1yNG1/8r/nMzEwNHDhQ33//vcLCwi66Tl0jyAAAYHFDhw7V4sWLVVFRofXr1ys1NVW+vr5KT0/3dGt1jktLAABYnL+/v+x2u9q0aaPx48crISFBa9as0ffff68777xTzZo1U5MmTZSUlKS8vDznzx0+fFjDhg1Ts2bNFBQUpG7dumn9+vU6dOiQBg78aWaoWbNmstlsGjt2rIc+3YUxIwMAQAMTGBiokydPauzYscrLy9OaNWsUEhKixx57TL/97W914MAB+fr6KjU1VZWVldq6dauCgoJ04MABBQcHKzo6Wu+9955SUlKUm5urkJAQBQYGevpjnRNBBgCABsIwDGVkZGjjxo1KSkrS6tWr9fHHH6t///6SpKVLlyo6OlqrV6/WrbfeqiNHjiglJUXdu3eXJLVr185ZKzw8XJLUsmVLr14jw6UlAAAsbt26dQoODlZAQICSkpI0atQojR07Vo0bN1bfvn2d50VERKhTp0764oufHtXx4IMPaubMmRowYICmTZumzz77zFMf4aIRZAAAsLiBAwcqJydHeXl5+uGHH/Tmm2/KZrP96s/98Y9/1Ndff60//OEP2r9/v3r37q1XXnmlHjo2D5eWvMCnBSWm1Wpy6pBptQAA1hAUFKT27du77OvSpYt+/PFHZWVlOS8tnTx5Urm5ueratavzvOjoaN1333267777lJ6ertdff10TJkyQn5+fJKm6urr+PshFYEYGAIAGqEOHDkpOTtY999yj7du369NPP9Xvf/97tW7dWsnJyZKkiRMnauPGjcrPz9fevXu1efNmdenSRZLUpk0b2Ww2rVu3Tt98841Onz7tyY9zXszIAABwAe4+oM6bLF68WA899JD+67/+S5WVlbrhhhu0fv16+fr6SvpptiU1NVVHjx5VSEiIhg4dqpdeekmS1Lp1a82YMUNTpkzRuHHjdOedd2rJkiUe/DTnZjMMw/B0E3XJ4XAoNDRUpaWlCgkJ8XQ757T9zVWm1fqmaIMpdX732F9NqQMAVlBeXq78/HzFxsYqICDA0+1cNswYdy4tAQAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAADwfxr4/S9ex4zxJsgAAC57P9+OfObMGQ93cnn5ebx/Hv+LwXNkAACXPR8fH4WFhenEiROSpCZNmtTqEf+4OIZh6MyZMzpx4oTCwsLk4+Nz0bUIMgAASLLb7ZLkDDOoe2FhYc5xv1gEGQAAJNlsNrVq1UotW7ZUVVWVp9tp8Hx9fS9pJuZnBBkAAP6Dj4+PKb9gUT9Y7AsAACyLIAMAACyLIAMAACyLIAMAACzL40Hm2LFj+v3vf6+IiAgFBgaqe/fu2rNnj/O4YRiaOnWqWrVqpcDAQCUkJCgvL8+DHQMAAG/h0SDz/fffa8CAAfL19dUHH3ygAwcO6IUXXlCzZs2c5zz77LN6+eWXtWDBAmVlZSkoKEiJiYkqLy/3YOcAAMAbePT26zlz5ig6OlqLFy927ouNjXX+2TAMzZ07V0888YSSk5MlSW+99ZYiIyO1evVq3XbbbfXeMwAA8B4enZFZs2aNevfurVtvvVUtW7bUNddco9dff915PD8/X0VFRUpISHDuCw0NVd++fbVz585z1qyoqJDD4XDZAABAw+TRIPP1119r/vz56tChgzZu3Kjx48frwQcf1JtvvilJKioqkiRFRka6/FxkZKTz2C/NmjVLoaGhzi06OrpuPwQAAPAYjwaZmpoa9erVS3/+8591zTXX6N5779U999yjBQsWXHTN9PR0lZaWOreCggITOwYAAN7Eo0GmVatW6tq1q8u+Ll266MiRI5L+/QVexcXFLucUFxef90um/P39FRIS4rIBAICGyaNBZsCAAcrNzXXZ969//Utt2rSR9NPCX7vdroyMDOdxh8OhrKwsxcfH12uvAADA+3j0rqVJkyapf//++vOf/6yRI0fqk08+0d/+9jf97W9/k/TTN5FOnDhRM2fOVIcOHRQbG6snn3xSUVFRGj58uCdbBwAAXsCjQebaa6/VqlWrlJ6erqeeekqxsbGaO3euRo8e7Tzn0UcfVVlZme69916VlJTouuuu04YNGxQQEODBzgEAgDewGYZheLqJuuRwOBQaGqrS0lKvXS+z/c1VptX6pmiDKXV+99hfTakDAEBd8vhXFAAAAFwsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsjwaZ6dOny2azuWydO3d2Hi8vL1dqaqoiIiIUHByslJQUFRcXe7BjAADgTTw+I9OtWzcVFhY6t+3btzuPTZo0SWvXrtXKlSu1ZcsWHT9+XCNGjPBgtwAAwJs0vtQC1dXV2r9/v9q0aaNmzZq530DjxrLb7WftLy0t1aJFi7Rs2TINGjRIkrR48WJ16dJFu3btUr9+/S61dQAAYHFuz8hMnDhRixYtkvRTiLnxxhvVq1cvRUdHKzMz0+0G8vLyFBUVpXbt2mn06NE6cuSIJCk7O1tVVVVKSEhwntu5c2fFxMRo586d561XUVEhh8PhsgEAgIbJ7SDz7rvvqmfPnpKktWvXKj8/X19++aUmTZqkP/3pT27V6tu3r5YsWaINGzZo/vz5ys/P1/XXX69Tp06pqKhIfn5+CgsLc/mZyMhIFRUVnbfmrFmzFBoa6tyio6Pd/YgAAMAi3L609O233zovBa1fv1633nqrOnbsqLvuukt/+ctf3KqVlJTk/HOPHj3Ut29ftWnTRu+8844CAwPdbU2SlJ6errS0NOdrh8NBmAEAoIFye0YmMjJSBw4cUHV1tTZs2KDf/OY3kqQzZ87Ix8fnkpoJCwtTx44ddfDgQdntdlVWVqqkpMTlnOLi4nOuqfmZv7+/QkJCXDYAANAwuR1kxo0bp5EjR+qqq66SzWZzrmHJyspyuXX6Ypw+fVpfffWVWrVqpbi4OPn6+iojI8N5PDc3V0eOHFF8fPwlvQ8AAGgY3L60NH36dF111VUqKCjQrbfeKn9/f0mSj4+PpkyZ4lathx9+WMOGDVObNm10/PhxTZs2TT4+Prr99tsVGhqqu+++W2lpaQoPD1dISIgmTJig+Ph47liqD7kfmFOnU9KvnwMAwEVyO8i89dZbGjVqlDPA/Oz222/X8uXL3ap19OhR3X777Tp58qRatGih6667Trt27VKLFi0kSS+99JIaNWqklJQUVVRUKDExUa+99pq7LQMAgAbKZhiG4c4P+Pj4qLCwUC1btnTZf/LkSbVs2VLV1dWmNnipHA6HQkNDVVpa6rXrZba/ucq0Wt8UbTClzu+GDzelDjMyAIC65PYaGcMwZLPZztp/9OhRhYaGmtIUAABAbdT60tI111zj/D6kwYMHq3Hjf/9odXW18vPzNXTo0DppEgAA4FxqHWSG/9+lhpycHCUmJio4ONh5zM/PT23btlVKSorpDcIzTn3yT1PqNDXp0lJmQaYpdW6KvsmUOgAA71DrIDNt2jRJUtu2bTVq1CgFBATUWVMAAAC14fZdS2PGjJEkVVZW6sSJE6qpqXE5HhMTY05nAAAAv8LtIJOXl6e77rpLO3bscNn/8yJgb7trCQAANFxuB5mxY8eqcePGWrdunVq1anXOO5gAAADqg9tBJicnR9nZ2Zf8dQQAAACXyu3nyHTt2lXffvttXfQCAADgFreDzJw5c/Too48qMzNTJ0+elMPhcNkAAADqi9uXln7+tuvBgwe77GexLwAAqG9uB5nNmzfXRR8AAABuczvI3HjjjXXRBwAAgNvcXiMjSdu2bdPvf/979e/fX8eOHZMk/e///q+2b99uanMAAAAX4naQee+995SYmKjAwEDt3btXFRUVkqTS0lL9+c9/Nr1BAACA83E7yMycOVMLFizQ66+/Ll9fX+f+AQMGaO/evaY2BwAAcCFuB5nc3FzdcMMNZ+0PDQ1VSUmJGT0BAADUittBxm636+DBg2ft3759u9q1a2dKUwAAALXhdpC555579NBDDykrK0s2m03Hjx/X0qVL9fDDD2v8+PF10SMAAMA5uX379ZQpU1RTU6PBgwfrzJkzuuGGG+Tv76+HH35YEyZMqIseG7zDZV+aVquJaZUAAPB+bgcZm82mP/3pT3rkkUd08OBBnT59Wl27dlVwcHBd9AcAAHBebgeZn/n5+alr165m9gIAAOCWWgWZESNG1Lrg+++/f9HNAAAAuKNWi31DQ0OdW0hIiDIyMrRnzx7n8ezsbGVkZCg0NLTOGgUAAPilWs3ILF682Pnnxx57TCNHjtSCBQvk4+MjSaqurtb999+vkJCQuukSAADgHNy+/fqNN97Qww8/7AwxkuTj46O0tDS98cYbpjYHAABwIW4HmR9//FFffnn27cJffvmlampqTGkKAACgNty+a2ncuHG6++679dVXX6lPnz6SpKysLM2ePVvjxo0zvUEAAIDzcTvIPP/887Lb7XrhhRdUWFgoSWrVqpUeeeQRTZ482fQG4Rn53542pU4PU6oAAHBubgeZRo0a6dFHH9Wjjz4qh8MhSSzyBQAAHnHRD8STCDAAAMCz3A4ysbGxstls5z3+9ddfX1JDAAAAteV2kJk4caLL66qqKu3bt08bNmzQI488YlZfl5Umpw55uoW6k/uBOXWaBJpTBwDQoLgdZB566KFz7p83b57L037dNXv2bKWnp+uhhx7S3LlzJUnl5eWaPHmyli9froqKCiUmJuq1115TZGTkRb8PAABoONx+jsz5JCUl6b333ruon929e7f++te/qkcP13tcJk2apLVr12rlypXasmWLjh8/7tb3PgEAgIbNtCDz7rvvKjw83O2fO336tEaPHq3XX39dzZo1c+4vLS3VokWL9OKLL2rQoEGKi4vT4sWLtWPHDu3atcustgEAgIW5fWnpmmuucVnsaxiGioqK9M033+i1115zu4HU1FTdfPPNSkhI0MyZM537s7OzVVVVpYSEBOe+zp07KyYmRjt37lS/fv3OWa+iokIVFRXO1z/fIg4AABoet4NMcnKyS5Bp1KiRWrRooZtuukmdO3d2q9by5cu1d+9e7d69+6xjRUVF8vPzU1hYmMv+yMhIFRUVnbfmrFmzNGPGDLf6AAAA1uR2kJk+fbopb1xQUKCHHnpImzZtUkBAgCk1JSk9PV1paWnO1w6HQ9HR0abVBwAA3sPtNTI+Pj46ceLEWftPnjzp8o3YvyY7O1snTpxQr1691LhxYzVu3FhbtmzRyy+/rMaNGysyMlKVlZUqKSlx+bni4mLZ7fbz1vX391dISIjLBgAAGia3Z2QMwzjn/oqKCvn5+dW6zuDBg7V//36XfePGjVPnzp312GOPKTo6Wr6+vsrIyFBKSookKTc3V0eOHFF8fLy7bQMAgAao1kHm5ZdfliTZbDYtXLhQwcHBzmPV1dXaunWrW2tkmjZtqquuusplX1BQkCIiIpz77777bqWlpSk8PFwhISGaMGGC4uPjz7vQFwAAXF5qHWReeuklST/NyCxYsMDlMpKfn5/atm2rBQsWmNrcSy+9pEaNGiklJcXlgXgAAACSG0EmPz9fkjRw4EC9//77Ls98MUtmZqbL64CAAM2bN0/z5s0z/b0AAID1ub1GZvPmzS6vf/zxR5WXl7tcagIAAKgPtb5rae3atVqyZInLvmeeeUbBwcEKCwvTkCFD9P3335vdHwAAwHnVOsi8+OKLKisrc77esWOHpk6dqieffFLvvPOOCgoK9PTTT9dJkwAAAOdS6yDzz3/+U/3793e+fvfdd/Wb3/xGf/rTnzRixAi98MILWrt2bZ00CQAAcC61DjKnTp1SRESE8/X27ds1ePBg5+tu3brp+PHj5nYHAABwAbUOMq1bt9YXX3wh6advrP70009dZmhOnjypJk2amN8hAADAedT6rqVbb71VEydO1OOPP67169fLbre7PJhuz5496tSpU500ifr3VWWhKXW++/6AKXXUJM6cOgCABqXWQWbq1Kk6duyYHnzwQdntdr399tsuD8X7+9//rmHDhtVJkwAAAOdS6yATGBiot95667zHf/l8GQAAgLrm9rdfAwAAeAuCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsKyLCjIPPPCAvvvuO7N7AQAAcEutb78+evSorrjiCknSsmXL9Oijjyo8PFzdu3fX+vXrFR0dXWdNAjqWbU6d6JvMqQMA8Aq1DjKdO3dWRESEBgwYoPLychUUFCgmJkaHDh1SVVVVXfYIAABwTrW+tFRSUqKVK1cqLi5ONTU1+u1vf6uOHTuqoqJCGzduVHFxcV32CQAAcJZaB5mqqir16dNHkydPVmBgoPbt26fFixfLx8dHb7zxhmJjY/muJQAAUK9qfWkpLCxMV199tQYMGKDKykr98MMPGjBggBo3bqwVK1aodevW2r17d132CgAA4KLWMzLHjh3TE088IX9/f/3444+Ki4vT9ddfr8rKSu3du1c2m03XXXddXfYKAADgotZBpnnz5ho2bJhmzZqlJk2aaPfu3ZowYYJsNpsefvhhhYaG6sYbb6zLXgEAAFxc9APxQkNDNXLkSPn6+uqjjz5Sfn6+7r//fjN7AwAAuKBar5H5T5999plat24tSWrTpo18fX1lt9s1atQoU5sDAAC4kIsKMv/58LvPP//ctGYAAADcwXctAQAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAy/JokJk/f7569OihkJAQhYSEKD4+Xh988IHzeHl5uVJTUxUREaHg4GClpKSouLjYgx0DAABv4tEgc8UVV2j27NnKzs7Wnj17NGjQICUnJ+uf//ynJGnSpElau3atVq5cqS1btuj48eMaMWKEJ1sGAABexGYYhuHpJv5TeHi4nnvuOd1yyy1q0aKFli1bpltuuUWS9OWXX6pLly7auXOn+vXrV6t6DodDoaGhKi0tVUhISF22ftFWzfkfT7dQZ5rd2NHTLbi4qd9kT7cAADCR16yRqa6u1vLly1VWVqb4+HhlZ2erqqpKCQkJznM6d+6smJgY7dy587x1Kioq5HA4XDYAANAwXdSXRppp//79io+PV3l5uYKDg7Vq1Sp17dpVOTk58vPzU1hYmMv5kZGRKioqOm+9WbNmacaMGXXcNWrLb99hU+pUXtPGlDoAgIbF4zMynTp1Uk5OjrKysjR+/HiNGTNGBw4cuOh66enpKi0tdW4FBQUmdgsAALyJx2dk/Pz81L59e0lSXFycdu/erb/85S8aNWqUKisrVVJS4jIrU1xcLLvdft56/v7+8vf3r+u2AQCAF/D4jMwv1dTUqKKiQnFxcfL19VVGRobzWG5uro4cOaL4+HgPdggAALyFR2dk0tPTlZSUpJiYGJ06dUrLli1TZmamNm7cqNDQUN19991KS0tTeHi4QkJCNGHCBMXHx9f6jiV4XrGjwpxCW/5lTh2T/upkFmSaUuem6JtMqQMAlyuPBpkTJ07ozjvvVGFhoUJDQ9WjRw9t3LhRv/nNbyRJL730kho1aqSUlBRVVFQoMTFRr732midbBgAAXsSjQWbRokUXPB4QEKB58+Zp3rx59dQRAACwEq9bIwMAAFBbBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZjT3dAAAvkvuBOXU6JZlTBwB+hUdnZGbNmqVrr71WTZs2VcuWLTV8+HDl5ua6nFNeXq7U1FRFREQoODhYKSkpKi4u9lDHAADAm3g0yGzZskWpqanatWuXNm3apKqqKg0ZMkRlZWXOcyZNmqS1a9dq5cqV2rJli44fP64RI0Z4sGsAAOAtPHppacOGDS6vlyxZopYtWyo7O1s33HCDSktLtWjRIi1btkyDBg2SJC1evFhdunTRrl271K9fP0+0DQAAvIRXLfYtLS2VJIWHh0uSsrOzVVVVpYSEBOc5nTt3VkxMjHbu3HnOGhUVFXI4HC4bAABomLxmsW9NTY0mTpyoAQMG6KqrrpIkFRUVyc/PT2FhYS7nRkZGqqio6Jx1Zs2apRkzZtR1u0CDlPn9AVPq3CQW+wKoH14zI5OamqrPP/9cy5cvv6Q66enpKi0tdW4FBQUmdQgAALyNV8zIPPDAA1q3bp22bt2qK664wrnfbrersrJSJSUlLrMyxcXFstvt56zl7+8vf3//um4ZAAB4AY/OyBiGoQceeECrVq3SRx99pNjYWJfjcXFx8vX1VUZGhnNfbm6ujhw5ovj4+PpuFwAAeBmPzsikpqZq2bJl+sc//qGmTZs6172EhoYqMDBQoaGhuvvuu5WWlqbw8HCFhIRowoQJio+P544leNaxbHPqRN9kTh0AuEx5NMjMnz9fknTTTTe57F+8eLHGjh0rSXrppZfUqFEjpaSkqKKiQomJiXrttdfquVMAAOCNPBpkDMP41XMCAgI0b948zZs3rx46AgAAVuI1dy0BAAC4iyADAAAsyytuvwasxm/fYXMKsWYdAC4JMzIAAMCyCDIAAMCyCDIAAMCyWCODy0vuB57uAABgImZkAACAZRFkAACAZRFkAACAZbFG5hKc+mizp1sAAOCyxowMAACwLIIMAACwLIIMAACwLIIMAACwLBb7Aheh2FHh6RYAAGJGBgAAWBhBBgAAWBZBBgAAWBZrZHBZeeNfu02p08yUKgCAS8WMDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCweiAfAyW/fYXMK9TOnDAD8GmZkAACAZRFkAACAZRFkAACAZbFG5hJ8WlDi6RYAUxU7KjzdgosPDxSbUieha6QpdQB4H4/OyGzdulXDhg1TVFSUbDabVq9e7XLcMAxNnTpVrVq1UmBgoBISEpSXl+eZZgEAgNfxaJApKytTz549NW/evHMef/bZZ/Xyyy9rwYIFysrKUlBQkBITE1VeXl7PnQIAAG/k0UtLSUlJSkpKOucxwzA0d+5cPfHEE0pOTpYkvfXWW4qMjNTq1at122231WerAADAC3ntYt/8/HwVFRUpISHBuS80NFR9+/bVzp07z/tzFRUVcjgcLhsAAGiYvHaxb1FRkSQpMtJ1kV5kZKTz2LnMmjVLM2bMqNPeANSP5sc/MqdQ19vNqWOSzF0vmFLnpn6TTakDWJnXzshcrPT0dJWWljq3goICT7cEAADqiNcGGbvdLkkqLna9/bK4uNh57Fz8/f0VEhLisgEAgIbJa4NMbGys7Ha7MjIynPscDoeysrIUHx/vwc4AAIC38OgamdOnT+vgwYPO1/n5+crJyVF4eLhiYmI0ceJEzZw5Ux06dFBsbKyefPJJRUVFafjw4Z5rGgAAeA2PBpk9e/Zo4MCBztdpaWmSpDFjxmjJkiV69NFHVVZWpnvvvVclJSW67rrrtGHDBgUEBHiqZQAA4EU8GmRuuukmGYZx3uM2m01PPfWUnnrqqXrsCgAAWIXXrpEBAAD4NQQZAABgWV77QDygLjQ7cMzTLQD6+psyU+rcZEoVwNqYkQEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJbFc2QuweGyL02p08SUKgAAXH6YkQEAAJZFkAEAAJZFkAEAAJZFkAEAAJbFYl+gATj10WZPtwAAHsGMDAAAsCyCDAAAsCyCDAAAsCzWyFyCJqcOeboFWNz2N1eZUqdndJgpdbzN3h/yTKlztSlVAHgjZmQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlsdgX8KCQ7z41p1D0jebUMYlZD+hrduCYKXU0zJwyZvG2z2XWf6+mgwaaUsc0uR+YU6dTkjl1UCeYkQEAAJZFkAEAAJZFkAEAAJbFGhmgAcj/dIunW3B1LNvTHcAN+3PNeTBjfy9bI7PjI3PWyPRvoGtkTv3v86bUafqHh02pc7GYkQEAAJZFkAEAAJZFkAEAAJbFGhnAg76qLPR0C3Vi/+kjnm7BhWnPSWldbkods6ya8z+ebsGrFTsqTKnz4YFiU+ok+Ow1pY5Zz7XJ//a0KXV6mFLl4lliRmbevHlq27atAgIC1LdvX33yySeebgkAAHgBrw8yK1asUFpamqZNm6a9e/eqZ8+eSkxM1IkTJzzdGgAA8DCvDzIvvvii7rnnHo0bN05du3bVggUL1KRJE73xxhuebg0AAHiYV6+RqaysVHZ2ttLT0537GjVqpISEBO3cufOcP1NRUaGKin9fFy0tLZUkORwO0/s7U15pek2gISjztZlSx6z/xxxlZabUMU6bs+aiof7bURf/zl4Ks8a50elTptRx+JwxpY5MGufT5eb8fa7r/+5NmzaVzXaBf1MML3bs2DFDkrFjxw6X/Y888ojRp0+fc/7MtGnTDElsbGxsbGxsDWArLS29YFbw6hmZi5Genq60tDTn65qaGn333XeKiIi4cKJzk8PhUHR0tAoKChQSEmJaXZyNsa4fjHP9YJzrB+NcP+pjnJs2bXrB414dZJo3by4fHx8VF7ve+lZcXCy73X7On/H395e/v7/LvrCwsLpqUSEhIfxPUk8Y6/rBONcPxrl+MM71w5Pj7NWLff38/BQXF6eMjAznvpqaGmVkZCg+Pt6DnQEAAG/g1TMykpSWlqYxY8aod+/e6tOnj+bOnauysjKNGzfO060BAAAP8/ogM2rUKH3zzTeaOnWqioqKdPXVV2vDhg2KjIz0aF/+/v6aNm3aWZexYD7Gun4wzvWDca4fjHP98IZxthmGYXjs3QEAAC6BV6+RAQAAuBCCDAAAsCyCDAAAsCyCDAAAsCyCzEWaN2+e2rZtq4CAAPXt21effPKJp1uytK1bt2rYsGGKioqSzWbT6tWrXY4bhqGpU6eqVatWCgwMVEJCgvLy8jzTrIXNmjVL1157rZo2baqWLVtq+PDhys3NdTmnvLxcqampioiIUHBwsFJSUs56KCUubP78+erRo4fzIWHx8fH64IMPnMcZ47oxe/Zs2Ww2TZw40bmPsTbH9OnTZbPZXLbOnTs7j3tynAkyF2HFihVKS0vTtGnTtHfvXvXs2VOJiYk6ceKEp1uzrLKyMvXs2VPz5s075/Fnn31WL7/8shYsWKCsrCwFBQUpMTFR5eXl9dyptW3ZskWpqanatWuXNm3apKqqKg0ZMkRl//GlipMmTdLatWu1cuVKbdmyRcePH9eIESM82LX1XHHFFZo9e7ays7O1Z88eDRo0SMnJyfrnP/8piTGuC7t379Zf//pX9ejRw2U/Y22ebt26qbCw0Llt377decyj42zO1zteXvr06WOkpqY6X1dXVxtRUVHGrFmzPNhVwyHJWLVqlfN1TU2NYbfbjeeee865r6SkxPD39zf+/ve/e6DDhuPEiROGJGPLli2GYfw0rr6+vsbKlSud53zxxReGJGPnzp2earNBaNasmbFw4ULGuA6cOnXK6NChg7Fp0ybjxhtvNB566CHDMPj7bKZp06YZPXv2POcxT48zMzJuqqysVHZ2thISEpz7GjVqpISEBO3cudODnTVc+fn5Kioqchnz0NBQ9e3blzG/RKWlpZKk8PBwSVJ2draqqqpcxrpz586KiYlhrC9SdXW1li9frrKyMsXHxzPGdSA1NVU333yzy5hK/H02W15enqKiotSuXTuNHj1aR44ckeT5cfb6J/t6m2+//VbV1dVnPVk4MjJSX375pYe6atiKiook6Zxj/vMxuK+mpkYTJ07UgAEDdNVVV0n6aaz9/PzO+qJVxtp9+/fvV3x8vMrLyxUcHKxVq1apa9euysnJYYxNtHz5cu3du1e7d+8+6xh/n83Tt29fLVmyRJ06dVJhYaFmzJih66+/Xp9//rnHx5kgA1ymUlNT9fnnn7tc54Z5OnXqpJycHJWWlurdd9/VmDFjtGXLFk+31aAUFBTooYce0qZNmxQQEODpdhq0pKQk55979Oihvn37qk2bNnrnnXcUGBjowc5Y7Ou25s2by8fH56zV2MXFxbLb7R7qqmH7eVwZc/M88MADWrdunTZv3qwrrrjCud9ut6uyslIlJSUu5zPW7vPz81P79u0VFxenWbNmqWfPnvrLX/7CGJsoOztbJ06cUK9evdS4cWM1btxYW7Zs0csvv6zGjRsrMjKSsa4jYWFh6tixow4ePOjxv9MEGTf5+fkpLi5OGRkZzn01NTXKyMhQfHy8BztruGJjY2W3213G3OFwKCsrizF3k2EYeuCBB7Rq1Sp99NFHio2NdTkeFxcnX19fl7HOzc3VkSNHGOtLVFNTo4qKCsbYRIMHD9b+/fuVk5Pj3Hr37q3Ro0c7/8xY143Tp0/rq6++UqtWrTz/d7rOlxM3QMuXLzf8/f2NJUuWGAcOHDDuvfdeIywszCgqKvJ0a5Z16tQpY9++fca+ffsMScaLL75o7Nu3zzh8+LBhGIYxe/ZsIywszPjHP/5hfPbZZ0ZycrIRGxtr/PDDDx7u3FrGjx9vhIaGGpmZmUZhYaFzO3PmjPOc++67z4iJiTE++ugjY8+ePUZ8fLwRHx/vwa6tZ8qUKcaWLVuM/Px847PPPjOmTJli2Gw24//9v/9nGAZjXJf+864lw2CszTJ58mQjMzPTyM/PNz7++GMjISHBaN68uXHixAnDMDw7zgSZi/TKK68YMTExhp+fn9GnTx9j165dnm7J0jZv3mxIOmsbM2aMYRg/3YL95JNPGpGRkYa/v78xePBgIzc317NNW9C5xliSsXjxYuc5P/zwg3H//fcbzZo1M5o0aWL87ne/MwoLCz3XtAXdddddRps2bQw/Pz+jRYsWxuDBg50hxjAY47r0yyDDWJtj1KhRRqtWrQw/Pz+jdevWxqhRo4yDBw86j3tynG2GYRh1P+8DAABgPtbIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAKh3Y8eO1fDhw8/an5mZKZvNdta36ALA+RBkAFxWqqqqPN0CABMRZAB4rffee0/dunWTv7+/2rZtqxdeeMHluM1m0+rVq132hYWFacmSJZKkQ4cOyWazacWKFbrxxhsVEBCgpUuX6vDhwxo2bJiaNWumoKAgdevWTevXr6+nTwXATI093QAAnEt2drZGjhyp6dOna9SoUdqxY4fuv/9+RUREaOzYsW7VmjJlil544QVdc801CggI0D333KPKykpt3bpVQUFBOnDggIKDg+vmgwCoUwQZAB6xbt26s8JDdXW1888vvviiBg8erCeffFKS1LFjRx04cEDPPfec20Fm4sSJGjFihPP1kSNHlJKSou7du0uS2rVrd5GfAoCncWkJgEcMHDhQOTk5LtvChQudx7/44gsNGDDA5WcGDBigvLw8l8BTG71793Z5/eCDD2rmzJkaMGCApk2bps8+++ziPwgAjyLIAPCIoKAgtW/f3mVr3bq1WzVsNpsMw3DZd67FvEFBQS6v//jHP+rrr7/WH/7wB+3fv1+9e/fWK6+84v6HAOBxBBkAXqlLly76+OOPXfZ9/PHH6tixo3x8fCRJLVq0UGFhofN4Xl6ezpw5U6v60dHRuu+++/T+++9r8uTJev31181rHkC9YY0MAK80efJkXXvttXr66ac1atQo7dy5U6+++qpee+015zmDBg3Sq6++qvj4eFVXV+uxxx6Tr6/vr9aeOHGikpKS1LFjR33//ffavHmzunTpUpcfB0AdYUYGgFfq1auX3nnnHS1fvlxXXXWVpk6dqqeeesploe8LL7yg6OhoXX/99brjjjv08MMPq0mTJr9au7q6WqmpqerSpYuGDh2qjh07ugQkANZhM355gRkAAMAimJEBAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACW9f8BV0fHUYJDNW4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H2a_pre_test_P3, 25, alpha=0.5, label='Pre advanced figure', range=(0,50))\n",
        "pyplot.hist(H2a_post_test, 25, alpha=0.5, label='Post advanced figure', range=(0,50))\n",
        "pyplot.legend(loc='upper right')\n",
        "stats.ttest_rel(H2a_pre_test_P2, H2a_pre_test_P3)\n",
        "pyplot.xlabel(\"Hours\")\n",
        "pyplot.ylabel(\"# Students\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "haS2MhgZ5eKP",
        "outputId": "639d2b1d-1a5f-4533-a647-4f41b9786ae3"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '# Students')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist((H2a_post_test - H2a_pre_test_P3), range = [-20,20], bins = 41, alpha=0.5, color = 'tab:green', label='Post - pre advanced figure')\n",
        "pyplot.legend(loc='upper right')\n",
        "pyplot.xlabel(\"Hours\")\n",
        "pyplot.ylabel(\"# Students\")\n",
        "pyplot.vlines(0,0,60,linestyle='--')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "_H26TZYu5oNo",
        "outputId": "7e8285ab-f0c7-48f0-f690-06155ad75c49"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.LineCollection at 0x7e43940e5ea0>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First estimation:\", H2a_pre_test_P1.mean())\n",
        "print(\"Second estimation:\", H2a_pre_test_P2.mean())\n",
        "print(\"Third estimation:\", H2a_pre_test_P3.mean())\n",
        "print(\"Actual time:\",H2a_post_test.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vy54KaqvPilX",
        "outputId": "7f9fd1f4-dca0-4449-d9c6-f6d77c658ded"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First estimation: 9.375379939209726\n",
            "Second estimation: 10.28419452887538\n",
            "Third estimation: 9.433130699088146\n",
            "Actual time: 8.538936170212768\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(stats.ttest_rel(H2a_pre_test_P1, H2a_post_test))\n",
        "print(stats.ttest_rel(H2a_pre_test_P2, H2a_post_test))\n",
        "print(stats.ttest_rel(H2a_pre_test_P3, H2a_post_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIWTkGvcNfSa",
        "outputId": "37bee62f-bb4e-49f2-de08-380c8283e482"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TtestResult(statistic=1.0323944471254027, pvalue=0.30264798964889444, df=328)\n",
            "TtestResult(statistic=3.5114609429406225, pvalue=0.0005080561239102739, df=328)\n",
            "TtestResult(statistic=2.1654533437885117, pvalue=0.031073426338039913, df=328)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#in total\n",
        "\n",
        "\n",
        "(H2a_pre_test_P3 + H1a_pre_test).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLrCbYFwUyuc",
        "outputId": "49c61a18-2073-4ce6-ec62-5eca6bc9e975"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18.4468085106383"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H2a_post_test.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBqwBYpkWNkW",
        "outputId": "e4bdb9ee-8293-4925-cd85-a27b00f822a5"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.538936170212768"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(H2a_post_test + H1a_post_test).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bOnVf4bmVMbB",
        "outputId": "e2572b64-1d5c-4f0e-8126-5c19d2bfadde"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19.070425531914893"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary:** *There is a significant difference between the time students take to perform the data analysis replication and the time they expect to take.*\n",
        "\n",
        "YES, but statistically significant for all the time we asked. However, students consistently **overestimated** the time it takes to reproduce the advanced figure.\n",
        "\n",
        "It takes less time they expected.\n"
      ],
      "metadata": {
        "id": "Ho0_DoPnQT5R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## H2b\n",
        "\n",
        "There is a significant difference between how challenging performing data analysis replication tasks is and how challenging students expect it to be."
      ],
      "metadata": {
        "id": "-ZeitpdqSaMI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "H2b_pre_testP1 = P1['3. How challenging do you expect it to be to reproduce figure B? '].apply(lambda r: int(r[0]))\n",
        "H2b_pre_testP2 = P2['3. How challenging do you expect it to be to reproduce figure B? '].apply(lambda r: int(r[0]))\n",
        "H2b_pre_testP3 = P3['3. How challenging do you expect it to be to reproduce figure B? '].apply(lambda r: int(r[0]))\n",
        "\n",
        "H2b_post_test = P4['3.  How challenging was it to reproduce figure B?'].apply(lambda r: int(r[0]))"
      ],
      "metadata": {
        "id": "mDMfIwTvQnkI"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First estimation:\", H2b_pre_testP1.mean())\n",
        "print(\"Second estimation:\", H2b_pre_testP2.mean())\n",
        "print(\"Third estimation:\", H2b_pre_testP3.mean())\n",
        "print(\"Actual effort:\", H2b_post_test.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cInISUfyeaDr",
        "outputId": "86b98c9b-fce8-44b8-b63e-c6a216923e04"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First estimation: 3.56838905775076\n",
            "Second estimation: 3.2066869300911853\n",
            "Third estimation: 3.0972644376899696\n",
            "Actual effort: 2.9088145896656536\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(stats.ttest_rel(H2b_pre_testP1, H2b_post_test))\n",
        "print(stats.ttest_rel(H2b_pre_testP2, H2b_post_test))\n",
        "print(stats.ttest_rel(H2b_pre_testP3, H2b_post_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WapXntZxes6D",
        "outputId": "54bc32ee-fdeb-4c17-d1da-54581fc9fdf1"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TtestResult(statistic=8.12077816946072, pvalue=9.483770825157575e-15, df=328)\n",
            "TtestResult(statistic=3.8297651666900805, pvalue=0.00015365743084541395, df=328)\n",
            "TtestResult(statistic=2.6831555934225935, pvalue=0.0076628436489968085, df=328)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H2b_pre_testP3, 5, range = [1,6], alpha=0.5, label='H2b_pre_test')\n",
        "pyplot.hist(H2b_post_test, 5, range = [1,6], alpha=0.5, label='H2b_post_test')\n",
        "pyplot.legend(loc='upper left')\n",
        "pyplot.xlabel(\"Score\")\n",
        "pyplot.ylabel(\"# Students\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "r3OFjMCw8tUC",
        "outputId": "c984c1b6-4998-43db-db6f-c02c10244a10"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '# Students')"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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aMmSIAUmv76mnntKPP/6oJ554Qvv27VP79u315ptvOvUcBQUF6t279xXlMCMjQ507d5abm5s2btyozz77TJGRkXrzzTfVuHFjZWZmOjUHAMD5KDu3uZSUFD322GOaPXu2Ro4cedVj9uzZo4sXL9of79ixw75mpjxSU1MdHu/YsUONGjWSm5ubmjZtqsuXLzscc+7cOR06dEiRkZH2sdDQUP3lL3/RmjVr9Oyzz2rJkiWSfv2ITbr+x3P/y8PD44rj27Ztq++++07h4eFXFEQfHx9JksViUXR0tKZNm6Zvv/1WHh4eWrt27TXnBAC4BsrObWzTpk167LHHNHr0aMXGxio7O1vZ2dk6f/68w3HFxcUaPny4Dhw4oE8//VRTpkxRfHx8udbrSNLx48c1btw4HTp0SO+++67efPNNJSQkSJIaNWqkPn36aMSIEdq6dav27NmjP//5z6pfv7769OkjSRozZow+//xzZWZmavfu3dq0aZOaNm0qSWrQoIEsFovWr1+vM2fOqKCg4IZ5wsPDlZqaqqNHj+rs2bMqKyvTqFGjdP78eQ0aNEhpaWk6cuSIPv/8c8XFxam0tFSpqamaNWuWdu7cqePHj2vNmjU6c+aMPUd4eLj27t2rQ4cO6ezZs/bF1QAA47FA+WYZdEdjZ1qxYoUuXLigpKQkJSX9vztCd+nSxeGr0927d1ejRo3UuXNnFRUVadCgQZo6dWq5zzNkyBBdvHhRHTp0kJubmxISEhyuIi1btkwJCQn6wx/+oOLiYnXu3FmffvqpatasKenXqzajRo3SiRMn5Ofnp549e2rOnDmSpPr162vatGl6/vnn7fcJWr58+XXzjB8/XkOHDlVkZKQuXryozMxMhYeH6+uvv9bEiRP1yCOPqKioSA0aNFDPnj1Vo0YN+fn5acuWLZo7d67y8/PVoEEDvf766+rVq5ckacSIEUpJSVH79u1VUFCgTZs2qWvXruV+jwAAlcdi+/0NT25T+fn5slqtysvLk5+fn8O+S5cuKTMzUxEREfLy8jIoYfXVtWtXtW7dWnPnzjU6ikvg7xPKa87Gw0ZHuC2MffheoyOgCnBlB+aQf8roBOVTXCJdypNS35bKLhid5uaY4KomgNsLZQc37fjx4w6LiP/XgQMHqjDNr45nnVBkx67X3H8gNUVhoXdVXSAAgOEoO7hpISEhSk9Pv+7+qv7ZhJDgIKV/tfG6+wEAtxfKDm6au7u7GjZsaHQMB+7u7mp4T4TRMQAALoSvnpcT67jhDGU2SbJJ/H0CgCrDlZ0bqFmzpiwWi86cOaO6des6/OwBXEixa9/XxmaTii+X6szP+apx+ZI8bJeMjgQAtw3Kzg24ubnprrvu0okTJ3T06FGj4+BaLuUZneDGbGWqVXxWYZd+UA1xZQcAqgplpxx8fX3VqFEj7orrylLfvvExBnOzlcjdViKuDQJA1aLslJObm5vc3NyMjoFrqa73rAEAVDoWKAMAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFMztOxs2bJFvXv3VkhIiCwWi9atW+ew32azafLkyQoODpa3t7diYmKUkZHhcMz58+c1ePBg+fn5yd/fX8OHD1dBQUEVvgoAAODKDC07hYWFatWqlRYsWHDV/a+++qrmz5+vRYsWKTU1VT4+PurRo4cuXbpkP2bw4MH67rvvtHHjRq1fv15btmzRyJEjq+olAAAAF+du5Ml79eqlXr16XXWfzWbT3Llz9dJLL6lPnz6SpHfeeUeBgYFat26dBg4cqIMHD2rDhg1KS0tT+/btJUlvvvmmHn30Ub322msKCQmpstcCAABck8uu2cnMzFR2drZiYmLsY1arVR07dtT27dslSdu3b5e/v7+96EhSTEyMatSoodTU1GvOXVRUpPz8fIcNAACYk8uWnezsbElSYGCgw3hgYKB9X3Z2turVq+ew393dXQEBAfZjriYpKUlWq9W+hYaGOjk9AABwFS5bdipTYmKi8vLy7FtWVpbRkQAAQCVx2bITFBQkScrJyXEYz8nJse8LCgrS6dOnHfZfvnxZ58+ftx9zNZ6envLz83PYAACAObls2YmIiFBQUJCSk5PtY/n5+UpNTVVUVJQkKSoqSrm5udq1a5f9mC+//FJlZWXq2LFjlWcGAACux9BvYxUUFOiHH36wP87MzFR6eroCAgIUFhamMWPGaObMmWrUqJEiIiI0adIkhYSEqG/fvpKkpk2bqmfPnhoxYoQWLVqkkpISxcfHa+DAgXwTCwAASDK47OzcuVPdunWzPx43bpwkaejQoVq+fLkmTJigwsJCjRw5Urm5uXrggQe0YcMGeXl52Z+zcuVKxcfHq3v37qpRo4ZiY2M1f/78Kn8tAADANVlsNpvN6BBGy8/Pl9VqVV5eHut3qqtNSUYnuH10SzQ6wW1hzsbDRke4LYx9+F6jI6AKuOyaHQAAAGeg7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFOj7AAAAFNzNzoA4AzbfzxndITbRlQ3oxMAQMVwZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJiaS5ed0tJSTZo0SREREfL29tY999yjGTNmyGaz2Y+x2WyaPHmygoOD5e3trZiYGGVkZBiYGgAAuBJ3owNcz+zZs7Vw4UKtWLFCzZo1086dOxUXFyer1arRo0dLkl599VXNnz9fK1asUEREhCZNmqQePXrowIED8vLyMvgVAMDNuf/4YqMj3CZeMzoAqoBLl51t27apT58+euyxxyRJ4eHhevfdd/XNN99I+vWqzty5c/XSSy+pT58+kqR33nlHgYGBWrdunQYOHGhYdgAA4Bpc+mOsTp06KTk5WYcPH5Yk7dmzR1u3blWvXr0kSZmZmcrOzlZMTIz9OVarVR07dtT27duvOW9RUZHy8/MdNgAAYE4ufWXn+eefV35+vpo0aSI3NzeVlpbq5Zdf1uDBgyVJ2dnZkqTAwECH5wUGBtr3XU1SUpKmTZtWecEBAIDLcOkrO++//75WrlypVatWaffu3VqxYoVee+01rVix4pbmTUxMVF5enn3LyspyUmIAAOBqXPrKznPPPafnn3/evvamRYsWOnbsmJKSkjR06FAFBQVJknJychQcHGx/Xk5Ojlq3bn3NeT09PeXp6Vmp2QEAgGtw6bJz4cIF1ajhePHJzc1NZWVlkqSIiAgFBQUpOTnZXm7y8/OVmpqqv/71r1UdF7gtzNl42OgIt4X7jQ4AmIhLl53evXvr5ZdfVlhYmJo1a6Zvv/1Wb7zxhp588klJksVi0ZgxYzRz5kw1atTI/tXzkJAQ9e3b19jwAADAJbh02XnzzTc1adIk/e1vf9Pp06cVEhKip59+WpMnT7YfM2HCBBUWFmrkyJHKzc3VAw88oA0bNnCPHQAAIEmy2H5/O+LbVH5+vqxWq/Ly8uTn52d0HNyE7f8cb3SE28aOsJFGR7gtcFPBqhE1nJsK3g5c+ttYAAAAt4qyAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATO2Wy05paanS09P1888/OyMPAACAU1W47IwZM0b//Oc/Jf1adLp06aK2bdsqNDRUKSkpzs4HAABwSypcdj788EO1atVKkvTJJ58oMzNT33//vcaOHasXX3zR6QEBAABuRYXLztmzZxUUFCRJ+vTTT9WvXz/de++9evLJJ7Vv3z6nBwQAALgVFS47gYGBOnDggEpLS7VhwwY9/PDDkqQLFy7Izc3N6QEBAABuhXtFnxAXF6f+/fsrODhYFotFMTExkqTU1FQ1adLE6QEBAABuRYXLztSpU9W8eXNlZWWpX79+8vT0lCS5ubnp+eefd3pAAACAW1HhsvPOO+9owIAB9pLzm0GDBum9995zWjAAAABnqPCanbi4OOXl5V0x/ssvvyguLs4poQAAAJylwmXHZrPJYrFcMX7ixAlZrVanhAIAAHCWcn+M1aZNG1ksFlksFnXv3l3u7v/vqaWlpcrMzFTPnj0rJSQAAMDNKnfZ6du3ryQpPT1dPXr0kK+vr32fh4eHwsPDFRsb6/SAAAAAt6LcZWfKlCmSpPDwcA0YMEBeXl6VFgoAAMBZKvxtrKFDh0qSiouLdfr0aZWVlTnsDwsLc04yAAAAJ6hw2cnIyNCTTz6pbdu2OYz/tnC5tLTUaeEAAABuVYXLzrBhw+Tu7q7169fb76IMAADgqipcdtLT07Vr1y5+GgIAAFQLFb7PTmRkpM6ePVsZWQAAAJyuwmVn9uzZmjBhglJSUnTu3Dnl5+c7bAAAAK6kwh9j/fYr5927d3cYZ4EyAABwRRUuO5s2baqMHAAAAJWiwmWnS5culZEDAACgUlR4zY4kffXVV/rzn/+sTp066eTJk5Kkf/3rX9q6datTwwEAANyqCped1atXq0ePHvL29tbu3btVVFQkScrLy9OsWbOcHhAAAOBWVLjszJw5U4sWLdKSJUtUs2ZN+3h0dLR2797t1HAAAAC3qsJl59ChQ+rcufMV41arVbm5uc7IBAAA4DQVLjtBQUH64YcfrhjfunWr7r77bqeEAgAAcJYKl50RI0YoISFBqampslgs+umnn7Ry5UqNHz9ef/3rXysjIwAAwE2r8FfPn3/+eZWVlal79+66cOGCOnfuLE9PT40fP17PPPNMZWQEAAC4aRUuOxaLRS+++KKee+45/fDDDyooKFBkZKR8fX0rIx8AAMAtqXDZ+Y2Hh4ciIyOdmQUAAMDpylV2Hn/88XJPuGbNmpsOAwAA4GzlWqBstVrtm5+fn5KTk7Vz5077/l27dik5OVlWq7XSggIAANyMcl3ZWbZsmf3PEydOVP/+/bVo0SK5ublJkkpLS/W3v/1Nfn5+lZMSAADgJlX4q+dLly7V+PHj7UVHktzc3DRu3DgtXbrUqeEAAABuVYXLzuXLl/X9999fMf7999+rrKzMKaEAAACcpcLfxoqLi9Pw4cN15MgRdejQQZKUmpqqV155RXFxcU4PCAAAcCsqXHZee+01BQUF6fXXX9epU6ckScHBwXruuef07LPPOj0gAADArahw2alRo4YmTJigCRMmKD8/X5JYmAwAAFzWTd9UUKLkAAAA11fhshMRESGLxXLN/T/++OMtBQIAAHCmCpedMWPGODwuKSnRt99+qw0bNui5555zVi67kydPauLEifrss8904cIFNWzYUMuWLVP79u0lSTabTVOmTNGSJUuUm5ur6OhoLVy4UI0aNXJ6FgAAUP1UuOwkJCRcdXzBggUOd1V2hp9//lnR0dHq1q2bPvvsM9WtW1cZGRm644477Me8+uqrmj9/vlasWKGIiAhNmjRJPXr00IEDB+Tl5eXUPAAAoPq5pTU7v9erVy8lJiY63G35Vs2ePVuhoaEOc0ZERNj/bLPZNHfuXL300kvq06ePJOmdd95RYGCg1q1bp4EDB1513qKiIhUVFdkf/7bQGgAAmE+Fbyp4LR9++KECAgKcNZ0k6eOPP1b79u3Vr18/1atXT23atNGSJUvs+zMzM5Wdna2YmBj7mNVqVceOHbV9+/ZrzpuUlOTwe1+hoaFOzQ0AAFxHha/stGnTxmGBss1mU3Z2ts6cOaO33nrLqeF+/PFHLVy4UOPGjdMLL7ygtLQ0jR49Wh4eHho6dKiys7MlSYGBgQ7PCwwMtO+7msTERI0bN87+OD8/n8IDAIBJVbjs9OnTx6Hs1KhRQ3Xr1lXXrl3VpEkTp4YrKytT+/btNWvWLEm/Fq39+/dr0aJFGjp06E3P6+npKU9PT2fFBAAALqzCZWfq1KmVEOPqgoODFRkZ6TDWtGlTrV69WpIUFBQkScrJyVFwcLD9mJycHLVu3brKcgIAANdV4TU7bm5uOn369BXj586dc/gldGeIjo7WoUOHHMYOHz6sBg0aSPp1sXJQUJCSk5Pt+/Pz85WamqqoqCinZgEAANVTha/s2Gy2q44XFRXJw8PjlgP93tixY9WpUyfNmjVL/fv31zfffKPFixdr8eLFkiSLxaIxY8Zo5syZatSokf2r5yEhIerbt69TswAAgOqp3GVn/vz5kn4tGP/4xz/k6+tr31daWqotW7Y4fc3Offfdp7Vr1yoxMVHTp09XRESE5s6dq8GDB9uPmTBhggoLCzVy5Ejl5ubqgQce0IYNG7jHDgAAkCRZbNe6VPM/fru/zbFjx3TXXXc5fGTl4eGh8PBwTZ8+XR07dqycpJUoPz9fVqtVeXl5/N5XNbX9n+ONjnDb2BE20ugIt4X7jy82OsJtIWr4a0ZHQBUo95WdzMxMSVK3bt20Zs0ah7sYAwAAuKoKr9nZtGmTw+PLly/r0qVLDh9rAQAAuIpyfxvrk08+0fLlyx3GXn75Zfn6+srf31+PPPKIfv75Z2fnAwAAuCXlLjtvvPGGCgsL7Y+3bdumyZMna9KkSXr//feVlZWlGTNmVEpIAACAm1Xuj7G+++47vfHGG/bHH374oR5++GG9+OKLkiQvLy8lJCQ4HAPAfFg4C6C6KfeVnV9++UV16tSxP966dau6d+9uf9ysWTP99NNPzk0HAABwi8pddurXr6+DBw9KkgoKCrRnzx516tTJvv/cuXOqVauW8xMCAADcgnKXnX79+mnMmDH617/+pREjRigoKEj333+/ff/OnTvVuHHjSgkJAABws8q9Zmfy5Mk6efKkRo8eraCgIP373/92uLHgu+++q969e1dKSAAAgJtV7rLj7e2td95555r7//f+OwAAAK6gwr96DgAAUJ1QdgAAgKlRdgAAgKlRdgAAgKlRdgAAgKndVNmJj4/X+fPnnZ0FAADA6cpddk6cOGH/86pVq1RQUCBJatGihbKyspyfDAAAwAnKfZ+dJk2aqE6dOoqOjtalS5eUlZWlsLAwHT16VCUlJZWZEQAA4KaV+8pObm6uPvjgA7Vr105lZWV69NFHde+996qoqEiff/65cnJyKjMnAADATSl32SkpKVGHDh307LPPytvbW99++62WLVsmNzc3LV26VBEREfw2FgAAcDnl/hjL399frVu3VnR0tIqLi3Xx4kVFR0fL3d1d//nPf1S/fn2lpaVVZlYAAIAKK/eVnZMnT+qll16Sp6enLl++rHbt2unBBx9UcXGxdu/eLYvFogceeKAyswIAAFRYucvOnXfeqd69eyspKUm1atVSWlqannnmGVksFo0fP15Wq1VdunSpzKwAAAAVdtM3FbRarerfv79q1qypL7/8UpmZmfrb3/7mzGwAAAC3rNxrdn5v7969ql+/viSpQYMGqlmzpoKCgjRgwACnhgMAALhVN1V2QkND7X/ev3+/08IAAAA4G7+NBQAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATM3d6ACmtynJ6AQAANzWuLIDAABMjbIDAABMrVp9jPXKK68oMTFRCQkJmjt3riTp0qVLevbZZ/Xee++pqKhIPXr00FtvvaXAwEBjwwIAXB9LDapGt0RDT19truykpaXp7bffVsuWLR3Gx44dq08++UQffPCBNm/erJ9++kmPP/64QSkBAICrqRZlp6CgQIMHD9aSJUt0xx132Mfz8vL0z3/+U2+88YYeeughtWvXTsuWLdO2bdu0Y8eOa85XVFSk/Px8hw0AAJhTtSg7o0aN0mOPPaaYmBiH8V27dqmkpMRhvEmTJgoLC9P27duvOV9SUpKsVqt9Cw0NrbTsAADAWC5fdt577z3t3r1bSUlXfq6anZ0tDw8P+fv7O4wHBgYqOzv7mnMmJiYqLy/PvmVlZTk7NgAAcBEuvUA5KytLCQkJ2rhxo7y8vJw2r6enpzw9PZ02HwAAcF0ufWVn165dOn36tNq2bSt3d3e5u7tr8+bNmj9/vtzd3RUYGKji4mLl5uY6PC8nJ0dBQUHGhAYAAC7Fpa/sdO/eXfv27XMYi4uLU5MmTTRx4kSFhoaqZs2aSk5OVmxsrCTp0KFDOn78uKKiooyIDAAAXIxLl53atWurefPmDmM+Pj6qU6eOfXz48OEaN26cAgIC5Ofnp2eeeUZRUVG6//77jYgMAABcjEuXnfKYM2eOatSoodjYWIebCgIAAEiSxWaz2YwOYbT8/HxZrVbl5eXJz8/PuZNzd84qsf3Hc0ZHAFANRd1dx+gItwfuoAwAAFB5KDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUXLrsJCUl6b777lPt2rVVr1499e3bV4cOHXI45tKlSxo1apTq1KkjX19fxcbGKicnx6DEAADA1bgbHeB6Nm/erFGjRum+++7T5cuX9cILL+iRRx7RgQMH5OPjI0kaO3as/vvf/+qDDz6Q1WpVfHy8Hn/8cX399dcGpwcAuLrtP54zOsJtIaqbsee32Gw2m7ERyu/MmTOqV6+eNm/erM6dOysvL09169bVqlWr9Kc//UmS9P3336tp06bavn277r///nLNm5+fL6vVqry8PPn5+Tk39KYk586Hq+L/sADAdUUNf83Q87v0x1j/Ky8vT5IUEBAgSdq1a5dKSkoUExNjP6ZJkyYKCwvT9u3brzlPUVGR8vPzHTYAAGBO1abslJWVacyYMYqOjlbz5s0lSdnZ2fLw8JC/v7/DsYGBgcrOzr7mXElJSbJarfYtNDS0MqMDAAADVZuyM2rUKO3fv1/vvffeLc+VmJiovLw8+5aVleWEhAAAwBW59ALl38THx2v9+vXasmWL7rrrLvt4UFCQiouLlZub63B1JycnR0FBQdecz9PTU56enpUZGQAAuAiXLjs2m03PPPOM1q5dq5SUFEVERDjsb9eunWrWrKnk5GTFxsZKkg4dOqTjx48rKirKiMhXYOEsAADGcumyM2rUKK1atUofffSRateubV+HY7Va5e3tLavVquHDh2vcuHEKCAiQn5+fnnnmGUVFRZX7m1gAAMDcXLrsLFy4UJLUtWtXh/Fly5Zp2LBhkqQ5c+aoRo0aio2NVVFRkXr06KG33nqripMCAABX5dJlpzy3APLy8tKCBQu0YMGCKkgEAACqm2rzbSwAAICbQdkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmRtkBAACmZpqys2DBAoWHh8vLy0sdO3bUN998Y3QkAADgAkxRdv7zn/9o3LhxmjJlinbv3q1WrVqpR48eOn36tNHRAACAwUxRdt544w2NGDFCcXFxioyM1KJFi1SrVi0tXbrU6GgAAMBg7kYHuFXFxcXatWuXEhMT7WM1atRQTEyMtm/fftXnFBUVqaioyP44Ly9PkpSfn+/0fIUXi258EAAAJlYZ/379vdq1a8tisVxzf7UvO2fPnlVpaakCAwMdxgMDA/X9999f9TlJSUmaNm3aFeOhoaGVkhEAgNvaM3+v1Onz8vLk5+d3zf3VvuzcjMTERI0bN87+uKysTOfPn1edOnWu2wwrKj8/X6GhocrKyrru/wi4NbzPVYf3umrwPlcN3ueqURXvc+3ata+7v9qXnTvvvFNubm7KyclxGM/JyVFQUNBVn+Pp6SlPT0+HMX9//8qKKD8/P/5BqgK8z1WH97pq8D5XDd7nqmHk+1ztFyh7eHioXbt2Sk5Oto+VlZUpOTlZUVFRBiYDAACuoNpf2ZGkcePGaejQoWrfvr06dOiguXPnqrCwUHFxcUZHAwAABjNF2RkwYIDOnDmjyZMnKzs7W61bt9aGDRuuWLRc1Tw9PTVlypQrPjKDc/E+Vx3e66rB+1w1eJ+rhiu8zxabzWYz7OwAAACVrNqv2QEAALgeyg4AADA1yg4AADA1yg4AADA1yk4l2LJli3r37q2QkBBZLBatW7fO6EimlJSUpPvuu0+1a9dWvXr11LdvXx06dMjoWKazcOFCtWzZ0n5DsKioKH322WdGxzK9V155RRaLRWPGjDE6iulMnTpVFovFYWvSpInRsUzp5MmT+vOf/6w6derI29tbLVq00M6dO6s8B2WnEhQWFqpVq1ZasGCB0VFMbfPmzRo1apR27NihjRs3qqSkRI888ogKCwuNjmYqd911l1555RXt2rVLO3fu1EMPPaQ+ffrou+++MzqaaaWlpentt99Wy5YtjY5iWs2aNdOpU6fs29atW42OZDo///yzoqOjVbNmTX322Wc6cOCAXn/9dd1xxx1VnsUU99lxNb169VKvXr2MjmF6GzZscHi8fPly1atXT7t27VLnzp0NSmU+vXv3dnj88ssva+HChdqxY4eaNWtmUCrzKigo0ODBg7VkyRLNnDnT6Dim5e7ufs2fFIJzzJ49W6GhoVq2bJl9LCIiwpAsXNmBaeTl5UmSAgICDE5iXqWlpXrvvfdUWFjIz7FUklGjRumxxx5TTEyM0VFMLSMjQyEhIbr77rs1ePBgHT9+3OhIpvPxxx+rffv26tevn+rVq6c2bdpoyZIlhmThyg5MoaysTGPGjFF0dLSaN29udBzT2bdvn6KionTp0iX5+vpq7dq1ioyMNDqW6bz33nvavXu30tLSjI5iah07dtTy5cvVuHFjnTp1StOmTdODDz6o/fv33/DXs1F+P/74oxYuXKhx48bphRdeUFpamkaPHi0PDw8NHTq0SrNQdmAKo0aN0v79+/ncvZI0btxY6enpysvL04cffqihQ4dq8+bNFB4nysrKUkJCgjZu3CgvLy+j45ja75cZtGzZUh07dlSDBg30/vvva/jw4QYmM5eysjK1b99es2bNkiS1adNG+/fv16JFi6q87PAxFqq9+Ph4rV+/Xps2bdJdd91ldBxT8vDwUMOGDdWuXTslJSWpVatWmjdvntGxTGXXrl06ffq02rZtK3d3d7m7u2vz5s2aP3++3N3dVVpaanRE0/L399e9996rH374wegophIcHHzFfxA1bdrUkI8MubKDastms+mZZ57R2rVrlZKSYtjCt9tRWVmZioqKjI5hKt27d9e+ffscxuLi4tSkSRNNnDhRbm5uBiUzv4KCAh05ckRPPPGE0VFMJTo6+orbgRw+fFgNGjSo8iyUnUpQUFDg8F8ImZmZSk9PV0BAgMLCwgxMZi6jRo3SqlWr9NFHH6l27drKzs6WJFmtVnl7exuczjwSExPVq1cvhYWF6ZdfftGqVauUkpKizz//3OhoplK7du0r1pv5+PioTp06rENzsvHjx6t3795q0KCBfvrpJ02ZMkVubm4aNGiQ0dFMZezYserUqZNmzZql/v3765tvvtHixYu1ePHiqg9jg9Nt2rTJJumKbejQoUZHM5WrvceSbMuWLTM6mqk8+eSTtgYNGtg8PDxsdevWtXXv3t32xRdfGB3rttClSxdbQkKC0TFMZ8CAAbbg4GCbh4eHrX79+rYBAwbYfvjhB6NjmdInn3xia968uc3T09PWpEkT2+LFiw3JYbHZbLaqr1gAAABVgwXKAADA1Cg7AADA1Cg7AADA1Cg7AADA1Cg7AADA1Cg7AADA1Cg7AADA1Cg7AADA1Cg7AADA1Cg7AFzWmTNn9Ne//lVhYWHy9PRUUFCQevTooa+//troaACqEX4IFIDLio2NVXFxsVasWKG7775bOTk5Sk5O1rlz5yrlfMXFxfLw8KiUuQEYhys7AFxSbm6uvvrqK82ePVvdunVTgwYN1KFDByUmJuqPf/yj/Zinn35agYGB8vLyUvPmzbV+/Xr7HKtXr1azZs3k6emp8PBwvf766w7nCA8P14wZMzRkyBD5+flp5MiRkqStW7fqwQcflLe3t0JDQzV69GgVFhZW3YsH4FSUHQAuydfXV76+vlq3bp2Kioqu2F9WVqZevXrp66+/1r///W8dOHBAr7zyitzc3CRJu3btUv/+/TVw4EDt27dPU6dO1aRJk7R8+XKHeV577TW1atVK3377rSZNmqQjR46oZ8+eio2N1d69e/Wf//xHW7duVXx8fFW8bACVgF89B+CyVq9erREjRujixYtq27atunTpooEDB6ply5b64osv1KtXLx08eFD33nvvFc8dPHiwzpw5oy+++MI+NmHCBP33v//Vd999J+nXKztt2rTR2rVr7cc89dRTcnNz09tvv20f27p1q7p06aLCwkJ5eXlV4isGUBm4sgPAZcXGxuqnn37Sxx9/rJ49eyolJUVt27bV8uXLlZ6errvuuuuqRUeSDh48qOjoaIex6OhoZWRkqLS01D7Wvn17h2P27Nmj5cuX268s+fr6qkePHiorK1NmZqbzXySASscCZQAuzcvLSw8//LAefvhhTZo0SU899ZSmTJmi8ePHO2V+Hx8fh8cFBQV6+umnNXr06CuODQsLc8o5AVQtyg6AaiUyMlLr1q1Ty5YtdeLECR0+fPiqV3eaNm16xVfUv/76a9177732dT1X07ZtWx04cEANGzZ0enYAxuBjLAAu6dy5c3rooYf073//W3v37lVmZqY++OADvfrqq+rTp4+6dOmizp07KzY2Vhs3blRmZqY+++wzbdiwQZL07LPPKjk5WTNmzNDhw4e1YsUK/f3vf7/hFaGJEydq27Ztio+PV3p6ujIyMvTRRx+xQBmoxriyA8Al+fr6qmPHjpozZ46OHDmikpIShYaGasSIEXrhhRck/bqAefz48Ro0aJAKCwvVsGFDvfLKK5J+vULz/vvva/LkyZoxY4aCg4M1ffp0DRs27LrnbdmypTZv3qwXX3xRDz74oGw2m+655x4NGDCgsl8ygErCt7EAAICp8TEWAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwtf8Pjym7XbORW78AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H2b_post_test - H2b_pre_testP3, 11, color = 'tab:green',range = [-6,6], alpha=0.5, label='H2b_post_test - H2b_pre_test')\n",
        "pyplot.legend(loc='upper left')\n",
        "pyplot.xlabel(\"Score\")\n",
        "pyplot.ylabel(\"# Students\")\n",
        "\n",
        "pyplot.vlines(0,0,180, linestyle='--')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "IMg7VCUH88bH",
        "outputId": "adca9f14-a41b-4497-a38e-2b2f50b7668b"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.LineCollection at 0x7e4393fbf7f0>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary:** *There is a significant difference between how challenging performing data analysis replication tasks is and how challenging students expect it to be.*\n",
        "\n",
        "YES. Statistically significant for all pre tests evaluations. Students overestimated how challenging it will be."
      ],
      "metadata": {
        "id": "pUInvposfJBj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## H2c\n",
        "There are discrepancies between the predicted and the true distribution of time spent on the three core activities: data wrangling, data analysis, and interpretation."
      ],
      "metadata": {
        "id": "P0iMEKgchGIA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "H2c_A_pre_P1 = P1['2. While attempting to reproduce figure B, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [A]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H2c_B_pre_P1 = P1['2. While attempting to reproduce figure B, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [B]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H2c_C_pre_P1 = P1['2. While attempting to reproduce figure B, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [C]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "\n",
        "H2c_A_pre_P2 = P2['2. While attempting to reproduce figure B, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [A]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H2c_B_pre_P2 = P2['2. While attempting to reproduce figure B, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [B]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H2c_C_pre_P2 = P2['2. While attempting to reproduce figure B, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [C]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "\n",
        "H2c_A_pre_P3 = P3['2. While attempting to reproduce figure B, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [A]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H2c_B_pre_P3 = P3['2. While attempting to reproduce figure B, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [B]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H2c_C_pre_P3 = P3['2. While attempting to reproduce figure B, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [C]'].apply(lambda r: r.split(\" \")[0][0])"
      ],
      "metadata": {
        "id": "neLTBg9zfA3E"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "H2c_pre_test_P1 = pd.DataFrame()\n",
        "H2c_pre_test_P1['A']=H2c_A_pre_P1\n",
        "H2c_pre_test_P1['B']=H2c_B_pre_P1\n",
        "H2c_pre_test_P1['C']=H2c_C_pre_P1\n",
        "H2c_pre_test_P1 = (H2c_pre_test_P1['A']+H2c_pre_test_P1['B']+H2c_pre_test_P1['C'])\n",
        "\n",
        "H2c_pre_test_P2 = pd.DataFrame()\n",
        "H2c_pre_test_P2['A']=H2c_A_pre_P2\n",
        "H2c_pre_test_P2['B']=H2c_B_pre_P2\n",
        "H2c_pre_test_P2['C']=H2c_C_pre_P2\n",
        "H2c_pre_test_P2 = (H2c_pre_test_P2['A']+H2c_pre_test_P2['B']+H2c_pre_test_P2['C'])\n",
        "\n",
        "H2c_pre_test_P3 = pd.DataFrame()\n",
        "H2c_pre_test_P3['A']=H2c_A_pre_P3\n",
        "H2c_pre_test_P3['B']=H2c_B_pre_P3\n",
        "H2c_pre_test_P3['C']=H2c_C_pre_P3\n",
        "H2c_pre_test_P3 = (H2c_pre_test_P3['A']+H2c_pre_test_P3['B']+H2c_pre_test_P3['C'])"
      ],
      "metadata": {
        "id": "iMft0mISjLmA"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "H2c_A_post = P4['2. While attempting to reproduce figure B, you spent some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you spent on them. More info on each activity is given below. [A]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H2c_B_post = P4['2. While attempting to reproduce figure B, you spent some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you spent on them. More info on each activity is given below. [B]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H2c_C_post = P4['2. While attempting to reproduce figure B, you spent some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you spent on them. More info on each activity is given below. [C]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "\n",
        "H2c_post_test = pd.DataFrame()\n",
        "H2c_post_test['A']=H2c_A_post\n",
        "H2c_post_test['B']=H2c_B_post\n",
        "H2c_post_test['C']=H2c_C_post\n",
        "H2c_post_test = (H2c_post_test['A']+H2c_post_test['B']+H2c_post_test['C'])\n",
        "H2c_post_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XLNuox-KjjkJ",
        "outputId": "e688c098-cd96-4f28-df67-a893e456c1cf"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TSF    78\n",
              "FST    74\n",
              "SFT    63\n",
              "TFS    62\n",
              "STF    27\n",
              "FTS    25\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pd.crosstab(H2c_pre_test_P1, H2c_post_test).values)\n",
        "print(SquareTable.homogeneity(SquareTable((pd.crosstab(H2c_pre_test_P1, H2c_post_test).values)), method=\"stuart_maxwell\"))\n",
        "\n",
        "print(pd.crosstab(H2c_pre_test_P2, H2c_post_test).values)\n",
        "print(SquareTable.homogeneity(SquareTable((pd.crosstab(H2c_pre_test_P2, H2c_post_test).values)), method=\"stuart_maxwell\"))\n",
        "\n",
        "print(pd.crosstab(H2c_pre_test_P3, H2c_post_test).values)\n",
        "print(SquareTable.homogeneity(SquareTable((pd.crosstab(H2c_pre_test_P3, H2c_post_test).values)), method=\"stuart_maxwell\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "idrZ31sakPVw",
        "outputId": "d9bccf48-ec95-4518-f73d-c7d465ceeef1"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[33  9 17 10 20 26]\n",
            " [ 9  4  2  7  6 11]\n",
            " [22  9 36  7 20 25]\n",
            " [ 0  0  0  2  2  1]\n",
            " [ 9  3  7  1  9 14]\n",
            " [ 1  0  1  0  5  1]]\n",
            "df          5\n",
            "pvalue      0.0\n",
            "statistic   91.47128821199104\n",
            "[[23  5  5  7  7  8]\n",
            " [ 4  3  5  4  2  5]\n",
            " [16  7 27  4 16 14]\n",
            " [ 5  3  1  2  3  9]\n",
            " [19  5 22  7 26 21]\n",
            " [ 7  2  3  3  8 21]]\n",
            "df          5\n",
            "pvalue      1.1212102394630996e-05\n",
            "statistic   30.60432923582059\n",
            "[[31  7 17  5 12 14]\n",
            " [ 5  9  1  5  2  5]\n",
            " [19  4 30  4 18 18]\n",
            " [ 1  0  1  6  1  4]\n",
            " [14  3 13  4 21 18]\n",
            " [ 4  2  1  3  8 19]]\n",
            "df          5\n",
            "pvalue      9.537761378464182e-07\n",
            "statistic   35.99095461997638\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx = {\"F\": 1, \"S\": 2, \"T\": 3}\n",
        "\n",
        "print(\"First estimation\")\n",
        "print(\"W was\", H2c_A_pre_P1.apply(lambda r: idx[r]).mean())\n",
        "print(\"A was\", H2c_B_pre_P1.apply(lambda r: idx[r]).mean())\n",
        "print(\"E was\", H2c_C_pre_P1.apply(lambda r: idx[r]).mean())\n",
        "print(\"-----\")\n",
        "\n",
        "print(\"Second estimation\")\n",
        "print(\"W was\", H2c_A_pre_P2.apply(lambda r: idx[r]).mean())\n",
        "print(\"A was\", H2c_B_pre_P2.apply(lambda r: idx[r]).mean())\n",
        "print(\"E was\", H2c_C_pre_P2.apply(lambda r: idx[r]).mean())\n",
        "print(\"-----\")\n",
        "\n",
        "print(\"Third estimation\")\n",
        "print(\"W was\", H2c_A_pre_P3.apply(lambda r: idx[r]).mean())\n",
        "print(\"A was\", H2c_B_pre_P3.apply(lambda r: idx[r]).mean())\n",
        "print(\"E was\", H2c_C_pre_P3.apply(lambda r: idx[r]).mean())\n",
        "\n",
        "print(\"-----\")\n",
        "print(\"W is\", H2c_A_post.apply(lambda r: idx[r]).mean())\n",
        "print(\"A is\", H2c_B_post.apply(lambda r: idx[r]).mean())\n",
        "print(\"E is\", H2c_C_post.apply(lambda r: idx[r]).mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lL1b6_0-vbem",
        "outputId": "1fb80a8f-4e51-43b5-9a2b-77b6e43cad47"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First estimation\n",
            "W was 1.6869300911854104\n",
            "A was 1.641337386018237\n",
            "E was 2.6717325227963524\n",
            "-----\n",
            "Second estimation\n",
            "W was 2.2006079027355625\n",
            "A was 1.580547112462006\n",
            "E was 2.2188449848024314\n",
            "-----\n",
            "Third estimation\n",
            "W was 1.9908814589665653\n",
            "A was 1.6170212765957446\n",
            "E was 2.39209726443769\n",
            "-----\n",
            "W is 2.1246200607902734\n",
            "A is 1.778115501519757\n",
            "E is 2.0972644376899696\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary:** *There are discrepancies between the predicted and the true distribution of time spent on the three core activities: data wrangling, data analysis, and interpretation.*\n",
        "\n",
        "YES. Statistically significant for all pre tests evaluations. Students underestimated how challengening Data wrangling will be."
      ],
      "metadata": {
        "id": "mYa8mr_BwM0h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## H2d\n",
        "There are discrepancies between predicted and true outcomes of the replication."
      ],
      "metadata": {
        "id": "NDsIQCjCvQbc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "H2d_a_pre_P3 = P3['a) The analysis will replicate exactly (the replication attempt will produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures) .1']\n",
        "H2d_b_pre_P3 = P3['b) The analysis will replicate qualitatively (the replication attempt will produce results that have small differences with the paper, but these will still agree with the abstract-level findings of the paper).1']\n",
        "H2d_c_pre_P3 = P3['c) The analysis won’t  replicate at all (the replication attempt will produce results that are in conflict with the abstract-level findings of the paper) .1']"
      ],
      "metadata": {
        "id": "0vBFTiakxRYt"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "P3[\"H2d_P3_sum\"] = (H2d_a_pre_P3+H2d_b_pre_P3+H2d_c_pre_P3)\n",
        "\n",
        "students_with_mistakes_P3 = set(P3[P3['H2d_P3_sum']!=100]['UID'])\n"
      ],
      "metadata": {
        "id": "cQ1a5PCq4kxJ"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "P3_filtered = P3[P3.UID.apply(lambda r: r not in students_with_mistakes_P3)]\n",
        "P3_filtered = P3_filtered.sort_values(\"UID\") #just in case\n",
        "H2d_a_pre_P3 = P3_filtered['a) The analysis will replicate exactly (the replication attempt will produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures) .1']\n",
        "H2d_b_pre_P3 = P3_filtered['b) The analysis will replicate qualitatively (the replication attempt will produce results that have small differences with the paper, but these will still agree with the abstract-level findings of the paper).1']\n",
        "H2d_c_pre_P3 = P3_filtered['c) The analysis won’t  replicate at all (the replication attempt will produce results that are in conflict with the abstract-level findings of the paper) .1']\n",
        "(H2d_a_pre_P3+H2d_b_pre_P3+H2d_c_pre_P3).value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lskULlbg9MkH",
        "outputId": "7c5e5140-50c2-4a3b-cba6-8239a67eaa76"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100.0    326\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H2d_a_pre_P3, 10, alpha=0.5, label='H2d pre - Replicate exactly')\n",
        "pyplot.hist(H2d_b_pre_P3, 10, alpha=0.5, label='H2d pre - Replicate qualitatively')\n",
        "pyplot.hist(H2d_c_pre_P3, 10,alpha=0.5, label='H2d pre - Won\\'t replicate')\n",
        "pyplot.legend(loc='upper left')\n",
        "pyplot.xlabel(\"% of papers\")\n",
        "pyplot.ylabel(\"# Students\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "_lTB6NW9-FVQ",
        "outputId": "d3959ad1-c189-405b-bb65-15a9df9b896e"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '# Students')"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx = {\"a\": 1, \"b\": 2, \"c\": 3}\n",
        "P4_filtered = P4[P4.UID.apply(lambda r: r not in students_with_mistakes_P3)]\n",
        "P4_filtered = P4_filtered.sort_values(\"UID\") #just in case\n",
        "H2d_post = P4_filtered['8. Did you manage to reproduce figure B?'].apply(lambda r: idx[r[0]])\n",
        "H2d_post.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aB0hnltW-TDB",
        "outputId": "bdfd1ab3-2a6d-4074-807a-1ff328b5c8f6"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8. Did you manage to reproduce figure B?\n",
              "2    238\n",
              "3     46\n",
              "1     42\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H2d_pre = H2d_a_pre_P3/100*1+H2d_b_pre_P3/100*2+H2d_c_pre_P3/100*3\n",
        "pyplot.hist(H2d_pre, 10, alpha=0.5, label='H2d_pre')\n",
        "pyplot.xlabel(\"Score\")\n",
        "pyplot.ylabel(\"# Students\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "2sEnX1Ft_NjM",
        "outputId": "834527bc-5d64-45b0-c283-c0fe4078897a"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '# Students')"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(stats.ttest_rel(H2d_pre, H2d_post))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGPqJlRv-75O",
        "outputId": "4fb5957a-7814-4127-85fc-6645bd068391"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TtestResult(statistic=-7.752431706319402, pvalue=1.1679329287291795e-13, df=325)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Pre-test average:\", H2d_pre.mean())\n",
        "print(\"Post-test average:\", H2d_post.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujJzDtNFACqR",
        "outputId": "7c41ced9-20eb-4614-d357-98494fee3626"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pre-test average: 1.7576656441717793\n",
            "Post-test average: 2.0122699386503067\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H2d_pre, 15, alpha=0.5, label='H1d_pre')\n",
        "pyplot.hist(H2d_post, 15, alpha=0.5, label='H1d_post')\n",
        "pyplot.legend(loc='upper left')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "M-rr6ZtlAZ5s",
        "outputId": "22ca3437-b81e-4664-d99a-626faf641222"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7e4393dfb190>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*YES*. Statistically significant. The students had more reproducibility problems with the advanced figure than expected."
      ],
      "metadata": {
        "id": "C6xIjN5Q_MAx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# H3 - Expectation shift\n",
        "\n",
        "The replication task affects the students' expectations on the fraction of peer-reviewed data science papers that are reproducible.\n"
      ],
      "metadata": {
        "id": "SlPIUzdEMkCA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "H3_a_pre = P1['The analysis would replicate exactly (the replication attempt would produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures)']\n",
        "H3_b_pre = P1['The analysis would replicate qualitatively (the replication attempt would produce results that have small differences with the paper, but these would still agree with the abstract-level findings of the paper) ']\n",
        "H3_c_pre = P1['The analysis wouldn’t  replicate at all (the replication attempt would produce results that are in conflict with the abstract-level findings of the paper)']\n",
        "\n",
        "H3_a_post = P4['The analysis would replicate exactly (the replication attempt would produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures)']\n",
        "H3_b_post = P4['The analysis would replicate qualitatively (the replication attempt would produce results that have small differences with the paper, but these would still agree with the abstract-level findings of the paper) ']\n",
        "H3_c_post = P4['The analysis wouldn’t  replicate at all (the replication attempt would produce results that are in conflict with the abstract-level findings of the paper)']"
      ],
      "metadata": {
        "id": "Ars1GHI7Rz8F"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "P1['H3_estimation_sum'] = H3_a_pre+H3_b_pre+H3_c_pre\n",
        "P4['H3_estimation_sum'] = H3_a_post+H3_b_post+H3_c_post\n",
        "\n",
        "students_with_mistakes = set(P1[P1['H3_estimation_sum']!=100].UID.to_list()\\\n",
        "                                +P4[P4['H3_estimation_sum']!=100].UID.to_list())\n",
        "\n",
        "print(\"Total students to discard: {}\".format(len(students_with_mistakes)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pk8f0bReSUf-",
        "outputId": "961c374b-7b70-4a3c-94b1-14c5b8ef322f"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total students to discard: 23\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H3_clean_data = P1[P1.UID.apply(lambda r: r not in students_with_mistakes)]\\\n",
        "                    .merge(P4, on=\"UID\")\n",
        "\n",
        "H3_a_pre = H3_clean_data['The analysis would replicate exactly (the replication attempt would produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures)_x']\n",
        "H3_b_pre = H3_clean_data['The analysis would replicate qualitatively (the replication attempt would produce results that have small differences with the paper, but these would still agree with the abstract-level findings of the paper) _x']\n",
        "H3_c_pre = H3_clean_data['The analysis wouldn’t  replicate at all (the replication attempt would produce results that are in conflict with the abstract-level findings of the paper)_x']\n",
        "\n",
        "H3_a_post = H3_clean_data['The analysis would replicate exactly (the replication attempt would produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures)_y']\n",
        "H3_b_post = H3_clean_data['The analysis would replicate qualitatively (the replication attempt would produce results that have small differences with the paper, but these would still agree with the abstract-level findings of the paper) _y']\n",
        "H3_c_post = H3_clean_data['The analysis wouldn’t  replicate at all (the replication attempt would produce results that are in conflict with the abstract-level findings of the paper)_y']"
      ],
      "metadata": {
        "id": "20Kbg8JlUL0A"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy\n",
        "\n",
        "H3_a_pre_mean = H3_a_pre.mean()\n",
        "H3_a_pre_boostrap = scipy.stats.bootstrap((H3_a_pre.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "print(H3_a_pre_mean, H3_a_pre_boostrap.confidence_interval.low, H3_a_pre_boostrap.confidence_interval.high)\n",
        "\n",
        "\n",
        "H3_b_pre_mean = H3_b_pre.mean()\n",
        "H3_b_pre_boostrap = scipy.stats.bootstrap((H3_b_pre.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "print(H3_b_pre_mean, H3_b_pre_boostrap.confidence_interval.low, H3_b_pre_boostrap.confidence_interval.high)\n",
        "\n",
        "\n",
        "H3_c_pre_mean = H3_c_pre.mean()\n",
        "H3_c_pre_boostrap = scipy.stats.bootstrap((H3_c_pre.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "print(H3_c_pre_mean, H3_c_pre_boostrap.confidence_interval.low, H3_c_pre_boostrap.confidence_interval.high)\n",
        "\n",
        "H3_a_post_mean = H3_a_post.mean()\n",
        "H3_a_post_boostrap = scipy.stats.bootstrap((H3_a_post.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "print(H3_a_post_mean, H3_a_post_boostrap.confidence_interval.low, H3_a_post_boostrap.confidence_interval.high)\n",
        "\n",
        "\n",
        "H3_b_post_mean = H3_b_post.mean()\n",
        "H3_b_post_boostrap = scipy.stats.bootstrap((H3_b_post.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "print(H3_b_post_mean, H3_b_post_boostrap.confidence_interval.low, H3_b_post_boostrap.confidence_interval.high)\n",
        "\n",
        "\n",
        "H3_c_post_mean = H3_c_post.mean()\n",
        "H3_c_post_boostrap = scipy.stats.bootstrap((H3_c_post.tolist(),), np.mean, n_resamples=10000, random_state=42, confidence_level=0.95)\n",
        "print(H3_c_post_mean, H3_c_post_boostrap.confidence_interval.low, H3_c_post_boostrap.confidence_interval.high)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6aJu_iHB5ApZ",
        "outputId": "1005ecc1-8a6a-4d6a-9c85-2536ef2ef167"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24.508169934640524 22.341503267973856 26.867311338198213\n",
            "58.611111111111114 56.377739322511786 60.74183006535948\n",
            "16.880718954248366 15.405228758169935 18.71078431372549\n",
            "26.65359477124183 24.163398692810457 29.263111554675298\n",
            "56.970588235294116 54.474920828201995 59.38779890873461\n",
            "16.375816993464053 14.987706416108887 17.99673202614379\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(5, 3))\n",
        "\n",
        "pyplot.hist(H3_a_pre, 10, alpha=0.3, label='Replicate exactly', range=(0,100))\n",
        "pyplot.hist(H3_b_pre, 10, alpha=0.3, label='Replicate qualitatively', range=(0,100))\n",
        "pyplot.hist(H3_c_pre, 10, alpha=0.3, label='Will not replicate', range=(0,100))\n",
        "pyplot.ylim((0,120))\n",
        "pyplot.xlabel(\"Percentage of papers\")\n",
        "pyplot.ylabel(\"Number of students\")\n",
        "pyplot.title(\"Pre-survey\")\n",
        "\n",
        "plt.axvline(x=H3_a_pre_mean, color=\"#1f77b4\", alpha=0.5, linestyle=\"--\")\n",
        "plt.axvline(x=H3_b_pre_mean, color=\"#ff7f0e\", alpha=0.5, linestyle=\"--\")\n",
        "plt.axvline(x=H3_c_pre_mean, color=\"#2ca02c\", alpha=0.5, linestyle=\"--\")\n",
        "\n",
        "ax.axvspan(H3_a_pre_boostrap.confidence_interval.low, H3_a_pre_mean, alpha=0.1, color='#1f77b4')\n",
        "ax.axvspan(H3_a_pre_boostrap.confidence_interval.high, H3_a_pre_mean, alpha=0.1, color='#1f77b4')\n",
        "\n",
        "ax.axvspan(H3_b_pre_boostrap.confidence_interval.low, H3_b_pre_mean, alpha=0.1, color='#ff7f0e')\n",
        "ax.axvspan(H3_b_pre_boostrap.confidence_interval.high, H3_b_pre_mean, alpha=0.1, color='#ff7f0e')\n",
        "\n",
        "ax.axvspan(H3_c_pre_boostrap.confidence_interval.low, H3_c_pre_mean, alpha=0.1, color='#2ca02c')\n",
        "ax.axvspan(H3_c_pre_boostrap.confidence_interval.high, H3_c_pre_mean, alpha=0.1, color='#2ca02c')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "plt.savefig(\"H3_1.pdf\",  bbox_inches=\"tight\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "S2iBleiJxx5S",
        "outputId": "f6376d61-ace4-4eaf-8284-fe9f9c8f1a39"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(5, 3))\n",
        "\n",
        "pyplot.hist(H3_a_post, 10, alpha=0.3, label='Replicate exactly', range=(0,100))\n",
        "pyplot.hist(H3_b_post, 10, alpha=0.3, label='Replicate qualitatively', range=(0,100))\n",
        "pyplot.hist(H3_c_post, 10, alpha=0.3, label='Will not replicate', range=(0,100))\n",
        "pyplot.ylim((0,120))\n",
        "\n",
        "pyplot.xlabel(\"Percentage of papers\")\n",
        "pyplot.ylabel(\"Number of students\")\n",
        "pyplot.title(\"Post-survey\")\n",
        "\n",
        "plt.axvline(x=H3_a_post_mean, color=\"#1f77b4\", alpha=0.5, linestyle=\"--\")\n",
        "plt.axvline(x=H3_b_post_mean, color=\"#ff7f0e\", alpha=0.5, linestyle=\"--\")\n",
        "plt.axvline(x=H3_c_post_mean, color=\"#2ca02c\", alpha=0.5, linestyle=\"--\")\n",
        "\n",
        "ax.axvspan(H3_a_post_boostrap.confidence_interval.low, H3_a_post_mean, alpha=0.1, color='#1f77b4')\n",
        "ax.axvspan(H3_a_post_boostrap.confidence_interval.high, H3_a_post_mean, alpha=0.1, color='#1f77b4')\n",
        "\n",
        "ax.axvspan(H3_b_post_boostrap.confidence_interval.low, H3_b_post_mean, alpha=0.1, color='#ff7f0e')\n",
        "ax.axvspan(H3_b_post_boostrap.confidence_interval.high, H3_b_post_mean, alpha=0.1, color='#ff7f0e')\n",
        "\n",
        "ax.axvspan(H3_c_post_boostrap.confidence_interval.low, H3_c_post_mean, alpha=0.1, color='#2ca02c')\n",
        "ax.axvspan(H3_c_post_boostrap.confidence_interval.high, H3_c_post_mean, alpha=0.1, color='#2ca02c')\n",
        "\n",
        "ax.legend(loc='upper right', framealpha=1, bbox_to_anchor=(1.12, 1), fontsize=9)\n",
        "\n",
        "\n",
        "plt.savefig(\"H3_2.pdf\",  bbox_inches=\"tight\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "dq7w74NrXUX3",
        "outputId": "8b5bebc8-4caf-4f12-9cfa-7ccbfedb5265"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H3_pre_weighted_sum = H3_a_pre*1+H3_b_pre*2+H3_c_pre*3\n",
        "H3_post_weighted_sum = H3_a_post*1+H3_b_post*2+H3_c_post*3\n",
        "\n",
        "print(\"Pre-test average\", H3_pre_weighted_sum.mean())\n",
        "print(\"Post-test average\", H3_post_weighted_sum.mean())\n",
        "\n",
        "print(stats.ttest_rel(H3_pre_weighted_sum, H3_post_weighted_sum))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZsETgDUgzoYY",
        "outputId": "e47ce797-1357-47fb-d164-a1e7c4e691d5"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pre-test average 192.37254901960785\n",
            "Post-test average 189.72222222222223\n",
            "TtestResult(statistic=1.4702601970345537, pvalue=0.1425221232975669, df=305)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Statistically **NOT** significant. There is no evidence of expectation change regarding the fraction of peer-reviewed data science papers that are reproducible.\n",
        "\n",
        "The aggregation method (weighted sum) may lose information."
      ],
      "metadata": {
        "id": "agHawb5k0kxm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#H4 - Spillover effect"
      ],
      "metadata": {
        "id": "LnTLhWspNKJp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## H4a\n",
        "There is a significant difference between the estimated time students expect the control paper needs before and after replicating their paper."
      ],
      "metadata": {
        "id": "J84lfcbt19oH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "H4a_P1 = P1['1. How many hours do you think it would take you to reproduce figure C?']\n",
        "H4a_P2 = P2['1. How many hours do you think it would take you to reproduce figure C?']"
      ],
      "metadata": {
        "id": "a91rkou2_Gh8"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H4a_P1, 50, range = [0,50], alpha=0.5, label='H4 - P1')\n",
        "pyplot.hist(H4a_P2, 50, range = [0,50], alpha=0.5, label='H4 - P2')\n",
        "pyplot.legend(loc='upper right')\n",
        "pyplot.xlabel(\"Hours\")\n",
        "pyplot.ylabel(\"# Students\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "SnnIgRWO00IE",
        "outputId": "c9661027-55a2-42f6-ba47-1d20406a368a"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '# Students')"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Pre-test average\", H4a_P1.mean())\n",
        "print(\"Post-test average\", H4a_P2.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_kP3vwcI1BGr",
        "outputId": "d81faec5-1597-495d-cbde-a7c51e3faae6"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pre-test average 8.80547112462006\n",
            "Post-test average 10.537993920972644\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H4a_P2.mean() - H4a_P1.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6BQuUrBY_TU",
        "outputId": "eefe8306-ec25-4e06-da18-39c51096e294"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.7325227963525833"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_rel(H4a_P1, H4a_P2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXysvCVM2UvE",
        "outputId": "5d0b488b-529c-45fe-b09a-f5e2bf398520"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TtestResult(statistic=-2.4976524613138573, pvalue=0.012991345735788355, df=328)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H4a_P1 = P1['1. How many hours do you think it would take you to reproduce figure D?']\n",
        "H4a_P2 = P2['1. How many hours do you think it would take you to reproduce figure D?']\n",
        "\n",
        "pyplot.hist(H4a_P1, 50, range = [0,50], alpha=0.5, label='H4 - P1')\n",
        "pyplot.hist(H4a_P2, 50, range = [0,50], alpha=0.5, label='H4 - P2')\n",
        "pyplot.legend(loc='upper right')\n",
        "pyplot.xlabel(\"Hours\")\n",
        "pyplot.ylabel(\"# Students\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "GKi4FiY6WAjf",
        "outputId": "701b2ea8-75b8-4ab0-eac4-9869923cdf42"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '# Students')"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Pre-test average\", H4a_P1.mean())\n",
        "print(\"Post-test average\", H4a_P2.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6Leq8hLWKD9",
        "outputId": "2b88734e-2d6a-4482-f28f-12a8d6ced66f"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pre-test average 8.75\n",
            "Post-test average 10.82370820668693\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H4a_P2.mean() - H4a_P1.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-ZSxr1vZJV6",
        "outputId": "97f65f73-8f2a-4f81-ed31-4b4cf5946929"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0737082066869306"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OnigDwW6ZMQ-"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_rel(H4a_P1, H4a_P2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdlTAs6aWSMW",
        "outputId": "dd940441-0935-4018-d79d-c432357ffbc4"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TtestResult(statistic=-3.552548235836075, pvalue=0.00043744707948839204, df=328)"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##H4b\n",
        "There is a significant difference between how challenging students expect replicating figure C to be."
      ],
      "metadata": {
        "id": "ZsYYnwgK2UDz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "H4b_P1 = P1['3. How challenging do you expect it to be to reproduce figure C? '].apply(lambda r: int(r[0]))\n",
        "H4b_P2 = P2['3. How challenging do you expect it to be to reproduce figure C? '].apply(lambda r: int(r[0]))"
      ],
      "metadata": {
        "id": "dzdbrc183Nii"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"P1 average\", H4b_P1.mean())\n",
        "print(\"P2 average\", H4b_P2.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TtNGvSkU3fGu",
        "outputId": "ca69a130-0e70-4cdb-d4d3-0b622bdbf52b"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P1 average 3.325227963525836\n",
            "P2 average 3.31306990881459\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"P1 median\", H4b_P1.median())\n",
        "print(\"P1 median\", H4b_P2.median())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eZnznTDj3oGz",
        "outputId": "cb7aa484-c60c-4a07-a0ed-c004cfc2c66a"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P1 median 3.0\n",
            "P1 median 3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H4b_P1, 5, range = [1,6], alpha=0.5, label='H4 P1')\n",
        "pyplot.hist(H4b_P2, 5, range = [1,6], alpha=0.5, label='H4 P2')\n",
        "pyplot.legend(loc='upper left')\n",
        "pyplot.xlabel(\"Score\")\n",
        "pyplot.ylabel(\"# Students\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "6cm_tOZE3vfh",
        "outputId": "f2c971a7-226b-4f96-93f5-db628ccb6cd3"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '# Students')"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_rel(H4b_P1, H4b_P2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFPouD-c36xT",
        "outputId": "6d19da39-ab3f-4dcb-d1d2-111b5f2501bb"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TtestResult(statistic=0.2065339262691591, pvalue=0.8365020125402169, df=328)"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H4b_P1 = P1['3. How challenging do you expect it to be to reproduce figure D? '].apply(lambda r: int(r[0]))\n",
        "H4b_P2 = P2['3. How challenging do you expect it to be to reproduce figure D? '].apply(lambda r: int(r[0]))"
      ],
      "metadata": {
        "id": "TY3trGRPWWjx"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"P1 average\", H4b_P1.mean())\n",
        "print(\"P2 average\", H4b_P2.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IprjmcL1Wa-G",
        "outputId": "cebd0ec9-13cc-4958-9e4b-4fb596483855"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P1 average 3.316109422492401\n",
            "P2 average 3.398176291793313\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H4b_P1, 5, range = [1,6], alpha=0.5, label='H4 P1')\n",
        "pyplot.hist(H4b_P2, 5, range = [1,6], alpha=0.5, label='H4 P2')\n",
        "pyplot.legend(loc='upper left')\n",
        "pyplot.xlabel(\"Score\")\n",
        "pyplot.ylabel(\"# Students\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "Pv8f7eH7WbYU",
        "outputId": "46f6d703-58a8-4690-c86c-a66a5ad2c4d6"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '# Students')"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_rel(H4b_P1, H4b_P2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7nb6nuxAZ29L",
        "outputId": "25d6b7dd-fa4c-4a53-a95d-2f6d12344120"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TtestResult(statistic=-1.4038366850852577, pvalue=0.16131356700118224, df=328)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##H4c\n",
        "There are pre/post test discrepancies between the expected time spent on the three core activities of the control paper: data wrangling, data analysis, and interpretation.\n",
        "\n"
      ],
      "metadata": {
        "id": "kDOwFULo4HrY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "H4c_A_pre = P1['2. While attempting to reproduce figure C, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [A]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H4c_B_pre = P1['2. While attempting to reproduce figure C, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [B]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H4c_C_pre = P1['2. While attempting to reproduce figure C, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [C]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "\n",
        "H4c_pre_test = pd.DataFrame()\n",
        "H4c_pre_test['A']=A_pre\n",
        "H4c_pre_test['B']=B_pre\n",
        "H4c_pre_test['C']=C_pre\n",
        "H4c_pre_test = (H4c_pre_test['A']+H4c_pre_test['B']+H4c_pre_test['C'])\n",
        "H4c_pre_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dgtGCQaz4a8R",
        "outputId": "bcd9f240-0fef-4e4c-bfcd-cd98415656bf"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FST    150\n",
              "SFT     89\n",
              "FTS     51\n",
              "TFS     23\n",
              "TSF      8\n",
              "STF      8\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H4c_A_post = P2['2. While attempting to reproduce figure C, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [A]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H4c_B_post = P2['2. While attempting to reproduce figure C, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [B]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H4c_C_post = P2['2. While attempting to reproduce figure C, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [C]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "\n",
        "H4c_post_test = pd.DataFrame()\n",
        "H4c_post_test['A']=H4c_A_post\n",
        "H4c_post_test['B']=H4c_B_post\n",
        "H4c_post_test['C']=H4c_C_post\n",
        "H4c_post_test = (H4c_post_test['A']+H4c_post_test['B']+H4c_post_test['C'])\n",
        "H4c_post_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hT_xk5JM41Y4",
        "outputId": "44cef8a1-b9eb-4685-bd8f-d104cf49f5de"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FST    104\n",
              "SFT     90\n",
              "TFS     49\n",
              "FTS     36\n",
              "TSF     30\n",
              "STF     20\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pd.crosstab(H4c_pre_test, H4c_post_test).values)\n",
        "print(SquareTable.homogeneity(SquareTable((pd.crosstab(H1c_pre_test,H1c_post_test).values)), method=\"stuart_maxwell\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLW3AK1B5JW1",
        "outputId": "d8e343ef-32a6-4d85-8a4b-bd28f9726c4c"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[63 13 33  5 22 14]\n",
            " [ 9 11 15  5  5  6]\n",
            " [23  9 32  7 13  5]\n",
            " [ 3  2  1  1  1  0]\n",
            " [ 4  1  8  0  7  3]\n",
            " [ 2  0  1  2  1  2]]\n",
            "df          5\n",
            "pvalue      0.0\n",
            "statistic   117.11642245832316\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx = {\"F\": 1, \"S\": 2, \"T\": 3}\n",
        "print(\"W was\", H4c_A_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"A was\", H4c_B_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"E was\", H4c_C_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"-----\")\n",
        "print(\"W is\", H4c_A_post.apply(lambda r: idx[r]).mean())\n",
        "print(\"A is\", H4c_B_post.apply(lambda r: idx[r]).mean())\n",
        "print(\"E is\", H4c_C_post.apply(lambda r: idx[r]).mean())\n",
        "print(\"-----\")\n",
        "print(\"W diff\", H4c_A_post.apply(lambda r: idx[r]).mean()-H4c_A_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"A diff\", H4c_B_post.apply(lambda r: idx[r]).mean()-H4c_B_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"E diff\", H4c_C_post.apply(lambda r: idx[r]).mean()-H4c_C_pre.apply(lambda r: idx[r]).mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GU8sf2bc5tNK",
        "outputId": "74c7825a-1cd2-475b-eb44-14b93da8521f"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "W was 1.4559270516717324\n",
            "A was 1.790273556231003\n",
            "E was 2.7537993920972643\n",
            "-----\n",
            "W is 1.8145896656534954\n",
            "A is 1.7477203647416413\n",
            "E is 2.4376899696048633\n",
            "-----\n",
            "W diff 0.358662613981763\n",
            "A diff -0.042553191489361764\n",
            "E diff -0.31610942249240104\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H4c_A_post = P2['2. While attempting to reproduce figure D, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [A]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H4c_B_post = P2['2. While attempting to reproduce figure D, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [B]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "H4c_C_post = P2['2. While attempting to reproduce figure D, you would spend some time on the following activities: (A) data wrangling, (B) data analysis and/or modeling, and (C) evaluating your results by comparing them with the results in the paper. Please sort these three activities by the order in the amount of time you expect to spend on them. More info on each activity is given below. [C]'].apply(lambda r: r.split(\" \")[0][0])\n",
        "\n",
        "H4c_post_test = pd.DataFrame()\n",
        "H4c_post_test['A']=H4c_A_post\n",
        "H4c_post_test['B']=H4c_B_post\n",
        "H4c_post_test['C']=H4c_C_post\n",
        "H4c_post_test = (H4c_post_test['A']+H4c_post_test['B']+H4c_post_test['C'])\n",
        "H4c_post_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0l6elaxSWjoj",
        "outputId": "2c3b40d2-7f78-44ca-eadb-e18c3b43cc9c"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FST    98\n",
              "SFT    90\n",
              "TFS    61\n",
              "FTS    41\n",
              "TSF    20\n",
              "STF    19\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pd.crosstab(H4c_pre_test, H4c_post_test).values)\n",
        "print(SquareTable.homogeneity(SquareTable((pd.crosstab(H1c_pre_test,H1c_post_test).values)), method=\"stuart_maxwell\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFm0CA-MWj9o",
        "outputId": "5c516f63-2b62-407c-803f-ed2522a8caa9"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[52 22 35  7 24 10]\n",
            " [14  7  8  5 12  5]\n",
            " [23  7 35  6 17  1]\n",
            " [ 2  3  2  0  1  0]\n",
            " [ 7  2  7  0  4  3]\n",
            " [ 0  0  3  1  3  1]]\n",
            "df          5\n",
            "pvalue      0.0\n",
            "statistic   117.11642245832316\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx = {\"F\": 1, \"S\": 2, \"T\": 3}\n",
        "print(\"W was\", H4c_A_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"A was\", H4c_B_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"E was\", H4c_C_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"-----\")\n",
        "print(\"W is\", H4c_A_post.apply(lambda r: idx[r]).mean())\n",
        "print(\"A is\", H4c_B_post.apply(lambda r: idx[r]).mean())\n",
        "print(\"E is\", H4c_C_post.apply(lambda r: idx[r]).mean())\n",
        "print(\"-----\")\n",
        "print(\"W diff\", H4c_A_post.apply(lambda r: idx[r]).mean()-H4c_A_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"A diff\", H4c_B_post.apply(lambda r: idx[r]).mean()-H4c_B_pre.apply(lambda r: idx[r]).mean())\n",
        "print(\"E diff\", H4c_C_post.apply(lambda r: idx[r]).mean()-H4c_C_pre.apply(lambda r: idx[r]).mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1yYPT_2yWvmS",
        "outputId": "68a7e5e7-fbdf-465e-a370-d62e27a2e695"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "W was 1.4559270516717324\n",
            "A was 1.790273556231003\n",
            "E was 2.7537993920972643\n",
            "-----\n",
            "W is 1.8237082066869301\n",
            "A is 1.7234042553191489\n",
            "E is 2.452887537993921\n",
            "-----\n",
            "W diff 0.3677811550151977\n",
            "A diff -0.06686930091185417\n",
            "E diff -0.3009118541033433\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##H4d\n",
        "There are discrepancies between predicted outcomes of the replication of the control paper before and after."
      ],
      "metadata": {
        "id": "YFAQw1ww6PRy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "H4d_a_P1 = P1['a) The analysis will replicate exactly (the replication attempt will produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures) .1']\n",
        "H4d_b_P1 = P1['b) The analysis will replicate qualitatively (the replication attempt will produce results that have small differences with the paper, but these will still agree with the abstract-level findings of the paper).1']\n",
        "H4d_c_P1 = P1['c) The analysis won’t  replicate at all (the replication attempt will produce results that are in conflict with the abstract-level findings of the paper) .1']\n",
        "\n",
        "H4d_a_P2 = P2['a) The analysis will replicate exactly (the replication attempt will produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures) .1']\n",
        "H4d_b_P2 = P2['b) The analysis will replicate qualitatively (the replication attempt will produce results that have small differences with the paper, but these will still agree with the abstract-level findings of the paper).1']\n",
        "H4d_c_P2 = P2['c) The analysis won’t  replicate at all (the replication attempt will produce results that are in conflict with the abstract-level findings of the paper) .1']"
      ],
      "metadata": {
        "id": "map3RT1z6CfC"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "P1['H4_estimation_sum'] = H4d_a_P1+H4d_b_P1+H4d_c_P1\n",
        "P4['H4_estimation_sum'] = H4d_a_P2+H4d_b_P2+H4d_c_P2\n",
        "\n",
        "students_with_mistakes = set(P1[P1['H4_estimation_sum']!=100].UID.to_list()\\\n",
        "                                +P4[P4['H4_estimation_sum']!=100].UID.to_list())\n",
        "\n",
        "print(\"Total students to discard: {}\".format(len(students_with_mistakes)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-SgV8e4BGhY",
        "outputId": "00196a3b-6d55-4a5d-9c64-0228b9668f62"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total students to discard: 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H4_clean_data = P1[P1.UID.apply(lambda r: r not in students_with_mistakes)]\\\n",
        "                    .merge(P4, on=\"UID\")\n",
        "\n",
        "\n",
        "H4d_a_P1 = H4_clean_data['a) The analysis will replicate exactly (the replication attempt will produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures) .1_x']\n",
        "H4d_b_P1 = H4_clean_data['b) The analysis will replicate qualitatively (the replication attempt will produce results that have small differences with the paper, but these will still agree with the abstract-level findings of the paper).1_x']\n",
        "H4d_c_P1 = H4_clean_data['c) The analysis won’t  replicate at all (the replication attempt will produce results that are in conflict with the abstract-level findings of the paper) .1_x']\n",
        "\n",
        "H4d_a_P2 = H4_clean_data['a) The analysis will replicate exactly (the replication attempt will produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures) .1_y']\n",
        "H4d_b_P2 = H4_clean_data['b) The analysis will replicate qualitatively (the replication attempt will produce results that have small differences with the paper, but these will still agree with the abstract-level findings of the paper).1_y']\n",
        "H4d_c_P2 = H4_clean_data['c) The analysis won’t  replicate at all (the replication attempt will produce results that are in conflict with the abstract-level findings of the paper) .1_y']"
      ],
      "metadata": {
        "id": "fBdQ59zwCDdD"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.hist(H4d_a_P1, 15, alpha=0.5, label='H4d P1 - Replicate exactly', range=(0,100))\n",
        "pyplot.hist(H4d_b_P1, 15, alpha=0.5, label='H4d P1 - Replicate qualitatively', range=(0,100))\n",
        "pyplot.hist(H4d_c_P1, 15, alpha=0.5, label='H4d P1 - Won\\'t replicate', range=(0,100))\n",
        "pyplot.ylim((0,120))\n",
        "pyplot.legend(loc='upper left')\n",
        "pyplot.xlabel(\"% of papers\")\n",
        "pyplot.ylabel(\"# Students\")\n",
        "pyplot.title(\"H4 - P1\")\n",
        "\n",
        "pyplot.show()\n",
        "\n",
        "pyplot.hist(H4d_a_P2, 15, alpha=0.5, label='H4d P2 - Replicate exactly', range=(0,100))\n",
        "pyplot.hist(H4d_b_P2, 15, alpha=0.5, label='H4d P2 - Replicate qualitatively', range=(0,100))\n",
        "pyplot.hist(H4d_c_P2, 15, alpha=0.5, label='H4d P2 - Won\\'t replicate', range=(0,100))\n",
        "pyplot.ylim((0,120))\n",
        "pyplot.legend(loc='upper left')\n",
        "pyplot.xlabel(\"% of papers\")\n",
        "pyplot.ylabel(\"# Students\")\n",
        "pyplot.title(\"H4 - P2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "id": "tbv94CPXCSrE",
        "outputId": "b9e96548-1d05-4dd1-803f-47400f1e86a7"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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bG2PBggWIj4+v0GetKEtLS/j6+mLjxo2Qy+U4ePAgYmNjVdI2Eb25GHbecHFxcUpTW3v27MG3336LM2fOwM7ODqamprCxsUF8fLw4+vP06VMkJibCzc3tpe3L5XKV3E4OPButedXAFBISgoYNG2Ls2LFwc3ODm5sbUlJSSoXB/zp79iw+//xzpeXnR0NepGXLloiJiVGa/inx4MEDpKSkYO3ateLo1KlTp5T2KRkBe/7fx8rKCra2tvjrr7/g5+dXoTqaNWsGfX19pKWloUuXLuXuN27cOGhpaeHgwYN4//338cEHH6B79+4Anl1o7eHhgeHDh4v7P38xtbGxMRwdHRETE6P0yIHn6erqlppGLcsXX3yBQYMGoV69emjYsCE6depUoc9JRFQehp03nLOzs9Ly+fPnoaWlpTSNMmbMGMybNw+NGzeGk5MTFi1apJIH52VkZCAjI0O8O+vSpUswNjaGg4MDzM3Nq9z+8+zt7dG7d2+EhYVh//79CAsLw4cffggHBwd8/PHH0NLSQnJyMi5fvoxZs2aJx0VHR6Nt27bo3LkztmzZgoSEBKxfv75CfU6ePBktWrTA8OHDMWzYMOjp6eHYsWPo168fzM3NYWFhgTVr1sDGxgZpaWkICQlROt7S0hKGhoY4dOgQ6tWrBwMDA5iamiI8PByjR4+GqakpfHx8UFBQgPPnz+PRo0cIDg4uVYexsTHGjx+PsWPHQqFQoHPnzsjOzsbp06dhYmICf39/HDhwABs2bEBcXBzc3NwwYcIE+Pv747fffkOdOnXQuHFjbN68GYcPH4ZcLsd3332Hc+fOiXeMAc8eEDhs2DBYWlrivffew+PHj3H69GmMGjUKAMQw1KlTJ+jr66NOnTplnjdvb2+YmJhg1qxZ+Oabbyp0romIXoR3Y9FLjRs3Dp999hn8/f3FKYzevXtXud1Vq1bB1dVVvPbE09MTrq6u2Lt3b5XbLsvYsWNx4MABJCQkwNvbG/v378cvv/yCdu3aoWPHjli8eDHq16+vdEx4eDi2bduGli1bYvPmzfjhhx/QrFmzCvXXpEkT/PLLL0hOTkb79u3h7u6OPXv2QEdHB1paWti2bRsSExPh4uKCsWPHYsGCBUrH6+joYNmyZVi9ejVsbW3Rs2dPAM9GPtatW4eNGzeiRYsW6NKlC6KiopSCx3/NnDkToaGhmDt3LpydneHj44MDBw5ALpfj3r17CAwMxIwZM8TRuvDwcFhZWWHYsGEAgK+++gp9+vTBgAED0KFDBzx48EBplAcA/P39sWTJEqxYsQLNmzfHhx9+qDQdFhERgSNHjsDe3v6Fo2NaWloICAhAcXGx0qgaEdGrkgkVfWCKhOXk5MDU1BTZ2dml7kjKz89Hamoq5HL5K13L8SoXFquCZS1LtfQrJXzir/oEBgbi3r171RZ8q0tVf14QUfXgNBYRaYzs7GxcunQJW7durXFBh4g0F8MOEWmMnj17IiEhAcOGDSt1qzwR0ati2CEqB2d4Xz/eZk5E1YEXKBMREZGkMewQERGRpDHsEBERkaQx7BAREZGkMewQERGRpDHsEBERkaQx7NArCQgIeOOfLHzz5k3IZDIkJSUBeHbbtEwmU8l7w2qqGTNmoHXr1uJydf538t/zrwr/rZ+IpIHP2XlVx+ZWaLfaRU9U0t2TzqMrtX9AQACysrKwe/dupfWxsbHo1q0bHj16VOpt5NevX4erqyu0tbWr/Au7pJ8SlpaW6Ny5MxYsWIAGDRoAANasWYOtW7fiwoULePz4cZk1vYqoqCgMHjwYwLNXPlhZWcHT0xMLFiyAg4NDldsvj4eHB9LT02FqaqqS9m7evAm5XI6LFy/W2F/AS5cuVXpeUdeuXdG6dWssWbKkUu2U9d+zvb090tPT8dZbb6moWiKSKo7sEACgqKgIgwYNwttvv63SdlNSUnDnzh1ER0fj999/h6+vL4qLiwEAeXl58PHxwZQpU1TaJwCYmJggPT0dt2/fxk8//YSUlBT069dP5f08T09PD9bW1pDJZNXaT01iamqqkgBbFm1tbVhbW0NHh3+zEdGLMewQAGDatGlwcnJC//79S20rLi5GcHAwzMzMYGFhgYkTJ1b46cKWlpawsbGBp6cnwsLCcOXKFVy/fh0AEBQUhJCQEHTs2FGlnwV4NqJjbW0NGxsbeHh4IDAwEAkJCcjJyRH32bNnD9zc3GBgYIAGDRogPDwcT58+VWpj5cqVeO+992BoaIgGDRpgx44d5fZZ1jTW6dOn0bVrV9SqVQt16tSBt7c3Hj16BAA4dOgQOnfuLJ7XDz/8EDdu3BCPLXmLuaurK2QyGbp27SpuW7duHZydnWFgYAAnJyesWLHihefjyZMn+Pzzz2FkZAQbGxtERESga9euCAoKUvq8/x0JNDMzQ1RUlLg8adIkNGnSBLVq1UKDBg0QGhqKoqKicvt9fhorICAAx48fx9KlSyGTySCTyXDz5k0UFxcjMDAQcrkchoaGaNq0KZYuXSq2MWPGDGzatAl79uwRj4uNjVWaxlIoFKhXrx5Wrlyp1P/FixehpaWFv//+GwCQlZWFL774AnXr1oWJiQm6d++O5OTkMms/ceIEdHV1kZGRobQ+KChI5X8UEFH1YtghHD16FNHR0YiMjCxze0REBKKiorBhwwacOnUKDx8+xK5duyrdj6GhIQCgsLCwSvVWVmZmJnbt2gVtbW1oa2sDAE6ePInPP/8cY8aMwZUrV7B69WpERUVh9uzZSseGhoaib9++SE5Ohp+fHwYOHIirV69WqN+kpCT06NEDzZo1Q1xcHE6dOqU0svXkyRMEBwfj/PnziImJgZaWFnr37g2FQgEASEhIAAD8+uuvSE9Px86dOwEAW7ZsQVhYGGbPno2rV69izpw5CA0NxaZNm8qtZcKECTh+/Dj27NmDX375BbGxsbhw4ULlTiQAY2NjREVF4cqVK1i6dCnWrl2LxYsXV+jYpUuXwt3dHUOHDkV6ejrS09Nhb28vBpXo6GhcuXIFYWFhmDJlCrZv3w4AGD9+PPr37w8fHx/xOA8PD6W2tbS0MGjQIGzdulVp/ZYtW9CpUyfUr18fANCvXz9kZmbi4MGDSExMhJubG3r06IGHDx+WqtfT0xMNGjTAd999J64rKirCli1bMGTIkEqdNyJSL47/Stj+/fthZGSktK7kF22JBw8eICAgAN9//z1MTEzKbGfJkiWYPHky+vTpAwBYtWoVDh8+XKla0tPTsXDhQtjZ2aFp06aVOvZVZGdnw8jICIIgIC8vDwAwevRo1K5dGwAQHh6OkJAQ+Pv7AwAaNGiAmTNnYuLEiZg+fbrYTr9+/fDFF18AAGbOnIkjR45g+fLlLx1JAYD58+ejbdu2Svs2b95c/L5v375K+2/YsAF169bFlStX4OLigrp16wIALCwsYG1tLe43ffp0REREiP8ecrlcDGwln+d5ubm5WL9+Pb7//nv06NEDALBp0ybUq1fvpZ/hv6ZNmyZ+7+joiPHjx2Pbtm2YOHHiS481NTWFnp4eatWqpfR5tLW1ER4eLi7L5XLExcVh+/bt6N+/P4yMjGBoaIiCggKl4/7Lz88PERERSEtLg4ODAxQKBbZt2ybWfOrUKSQkJCAzMxP6+voAgIULF2L37t3YsWMHvvzyy1JtBgYGYuPGjZgwYQIAYN++fcjPzy9zBJSINBdHdiSsW7duSEpKUvpat26d0j5Dhw7FJ598Ak9PzzLbyM7ORnp6Ojp06CCu09HRQdu2bStUQ7169VC7dm3Y2triyZMn+Omnn6Cnp/dKn2fLli0wMjISv06ePFnuvsbGxkhKSsL58+cREREBNzc3pVGb5ORkfPPNN0rtlYw4lIQjAHB3d1dq193dvdIjO+W5du0aBg0ahAYNGsDExASOjo4AgLS0tHKPefLkCW7cuIHAwECl2mfNmqU0Bfa8GzduoLCwUOnf0Nzc/JVC548//ohOnTrB2toaRkZGmDZt2gvrrajIyEi0adMGdevWhZGREdasWVPpdlu3bg1nZ2dxdOf48ePIzMwUr9VKTk5Gbm4uLCwslM5dampquecuICAA169fx9mzZwE8u/i9f//+YmgmopqBIzsSVrt2bTRq1Ehp3T///KO0fPToUezduxcLFy4E8OxN3wqFAjo6OlizZk2p0YfKOnnyJExMTGBpaQljY+MqtfXRRx8p/cK2s7Mrd18tLS3xszs7O+PGjRv4+uuvxSmJ3NxchIeHi6MjzzMwMKhSnSVKpu3K4+vri/r162Pt2rWwtbWFQqGAi4vLC6f5cnNzAQBr165VOhcAxCm6VyWTyUpdi/X89ThxcXHw8/NDeHg4vL29YWpqim3btiEiIqJK/W7btg3jx49HREQE3N3dYWxsjAULFiA+Pr7Sbfn5+WHr1q0ICQnB1q1b4ePjAwsLCwDPzp2NjU2Zb1Yv7yJqS0tL+Pr6YuPGjZDL5Th48CDfzE5UAzHsvOHi4uKUprb27NmDb7/9FmfOnIGdnR1MTU1hY2OD+Ph4cfTn6dOn4vUOLyOXy1V2N46xsfErB6aQkBA0bNgQY8eOhZubG9zc3JCSklIqDP7X2bNn8fnnnystu7q6VqjPli1bIiYmRmmKpsSDBw+QkpKCtWvXihe7njp1SmmfkhGw5/99rKysYGtri7/++gt+fn4VqqNhw4bQ1dVFfHy8eOv9o0eP8Oeff6JLly7ifnXr1kV6erq4fO3aNaVRrjNnzqB+/fqYOnWquK7kwt+K0tPTKzWVevr0aXh4eGD48OHiuv+OtJR1XFk++eQTTJs2DYmJidixYwdWrVolbnNzc0NGRgZ0dHTEUbSK+OKLLzBo0CDUq1cPDRs2RKdOnSp8LBFpBoadN5yzs7PS8vnz56GlpQUXFxdx3ZgxYzBv3jw0btwYTk5OWLRokUoenJeRkYGMjAzx7qxLly7B2NgYDg4OMDc3r3L7z7O3t0fv3r0RFhaG/fv3IywsDB9++CEcHBzw8ccfQ0tLC8nJybh8+TJmzZolHhcdHY22bduic+fO2LJlCxISErB+/foK9Tl58mS0aNECw4cPx7Bhw6Cnp4djx46hX79+MDc3h4WFBdasWQMbGxukpaUhJCRE6XhLS0sYGhri0KFDqFevHgwMDGBqaorw8HCMHj0apqam8PHxQUFBAc6fP49Hjx4hODi4VB1GRkYIDAzEhAkTYGFhAUtLS0ydOhVaWsqz2N27d8f//vc/uLu7o7i4GJMmTYKurq64vXHjxkhLS8O2bdvQrl07HDhwoNIXqjs6OiI+Ph43b96EkZERzM3N0bhxY2zevBmHDx+GXC7Hd999h3Pnzol3o5Ucd/jwYaSkpMDCwqLcZxk5OjqKd98VFxfjo48+Erd5eXnB3d0dvXr1wvz589GkSRPcuXMHBw4cQO/evcudmvX29oaJiQlmzZqFb775plKfl4g0A6/ZoZcaN24cPvvsM/j7+4vTDL17965yu6tWrYKrqyuGDh0K4NndL66urti7d2+V2y7L2LFjceDAASQkJMDb2xv79+/HL7/8gnbt2qFjx45YvHixeNdOifDwcGzbtg0tW7bE5s2b8cMPP6BZs2YV6q9Jkyb45ZdfkJycjPbt28Pd3R179uyBjo4OtLS0sG3bNiQmJsLFxQVjx47FggULlI7X0dHBsmXLsHr1atja2qJnz54Ano00rFu3Dhs3bkSLFi3QpUsXREVFKYWD/1qwYAHefvtt+Pr6wsvLC507d0abNm2U9omIiIC9vT3efvttfPLJJxg/fjxq1aolbv/oo48wduxYjBw5Eq1bt8aZM2cQGhpaoXNRYvz48dDW1kazZs1Qt25dpKWl4auvvkKfPn0wYMAAdOjQAQ8ePFAa5QGeXVvWtGlTtG3bFnXr1sXp06fL7cPPzw/Jycno3bu30lSiTCbDzz//DE9PTwwePBhNmjTBwIED8ffff8PKyqrc9rS0tBAQEIDi4mKlUT4iqjlkQkUfmCJhOTk5MDU1RXZ2dqk7kvLz85Gamgq5XP5K13Jk5mWqqsxKsaxlqZZ+pUQmk2HXrl2SfS3Gqz7N+E0UGBiIe/fuvTSIV/XnBRFVD05jERGVIzs7G5cuXcLWrVurbcSRiKofww4RUTl69uyJhIQEDBs2DO+88466yyGiV8SwQ1QOqc/w8hbql+M5IpIGXqBMREREksawQ0RERJLGsENERESSxrBDREREksawQ0RERJLGsENERESSxrBDryQgIECyTxaWkv/+O3Xt2hVBQUFqq4eISB34nJ1XtCJpRYX2e1L0RCX9+Tf3r9T+AQEByMrKwu7du5XWx8bGolu3bnj06FGpt5Ffv34drq6u0NbWrvKLPkv6KWFpaYnOnTtjwYIFaNCgAQBgzZo12Lp1Ky5cuIDHjx+XWVNl/fHHH3B2dkZcXBw6duworu/YsSOSkpKQlZUlPsY/Pz8fZmZmiIyMRGBgYJX6BYCbN29CLpeLz+eJiopCUFCQSl6aqio7d+5UerlnVZX33xkRkSZR68jOiRMn4OvrC1tbW8hkslI/MAVBQFhYGGxsbGBoaAgvLy9cu3ZNaZ+HDx/Cz88PJiYmMDMzQ2BgIHJzc1/jp5CGoqIiDBo0CG+//bZK201JScGdO3cQHR2N33//Hb6+viguLgYA5OXlwcfHB1OmTFFZf05OTrC2tlZ6GNzjx49x4cIF1K1bF2fPnhXXx8XFoaCgAN27d1dZ/6pQWFhYbW2bm5vD2Ni42tonItJEag07T548QatWrRAZGVnm9vnz52PZsmVYtWoV4uPjUbt2bXh7eyM/P1/cx8/PD7///juOHDmC/fv348SJE/jyyy9f10eQjGnTpsHJyQn9+/cvta24uBjBwcEwMzODhYUFJk6cWOGnC1taWsLGxgaenp4ICwvDlStXcP36dQBAUFAQQkJClEZgVKFbt25KYefUqVNo0qQJfH19ldbHxsaifv364tvCV65ciYYNG0JPTw9NmzbFd999p9SuTCbDunXr0Lt3b9SqVQuNGzcu931JsbGxGDx4MLKzsyGTySCTyTBjxowy950xYwZat26NdevWKb1AMisrC1988QXq1q0LExMTdO/eHcnJyaWOW716Nezt7VGrVi30798f2dnZ5Z6b/05jFRQUYNKkSbC3t4e+vj4aNWqE9evXA3j27x4YGAi5XA5DQ0M0bdoUS5cuVep/06ZN2LNnj/gZS87vrVu30L9/f5iZmcHc3Bw9e/bEzZs3y62LiKg6qTXsvPfee5g1axZ69+5dapsgCFiyZAmmTZuGnj17omXLlti8eTPu3LkjjgBdvXoVhw4dwrp169ChQwd07twZy5cvx7Zt23Dnzp3X/GlqrqNHjyI6Orrc0BkREYGoqChs2LABp06dwsOHD7Fr165K92NoaAigekcugGdh59SpU3j69CkA4NixY+jatSu6dOmCY8eOifsdO3ZMnGrbtWsXxowZg3HjxuHy5cv46quvMHjwYKX9ASA8PBz9+/fHb7/9hvfffx9+fn54+PBhqRo8PDywZMkSmJiYID09Henp6Rg/fny5NV+/fh0//fQTdu7ciaSkJABAv379kJmZiYMHDyIxMRFubm7o0aOHUn/Xr1/H9u3bsW/fPhw6dAgXL17E8OHDK3yuPv/8c/zwww9YtmwZrl69itWrV8PIyAgAoFAoUK9ePURHR+PKlSsICwvDlClTsH37dgDA+PHj0b9/f/j4+Iif0cPDA0VFRfD29oaxsTFOnjyJ06dPw8jICD4+PtX+b09EVBaNvWYnNTUVGRkZ8PLyEteZmpqiQ4cOiIuLw8CBAxEXFwczMzO0bdtW3MfLywtaWlqIj48vM0S9Sfbv3y/+4ipRMoVU4sGDBwgICMD3338PExOTMttZsmQJJk+ejD59+gAAVq1ahcOHD1eqlvT0dCxcuBB2dnZo2rRppY6trG7duuHJkyc4d+4c3N3dERsbiwkTJqBz587w9/dHfn4+BEFAQkICvvjiCwDAwoULERAQIAaF4OBgnD17FgsXLlS69iggIACDBg0CAMyZMwfLli1DQkICfHx84OjoKI546enpwdTUFDKZDNbW1i+tubCwEJs3b0bdunUBPBuNSkhIQGZmJvT19cUad+/ejR07doijl/n5+di8eTPs7OwAAMuXL8cHH3yAiIiIl/b7559/Yvv27Thy5Ij4/1nJ9VQAoKuri/DwcHFZLpcjLi4O27dvR//+/WFkZARDQ0MUFBQo9fX9999DoVBg3bp1kMlkAICNGzfCzMwMsbGxePfdd196PoiIVElj78bKyMgAAFhZWSmtt7KyErdlZGTA0tJSabuOjg7Mzc3FfcpSUFCAnJwcpS8p6tatG5KSkpS+1q1bp7TP0KFD8cknn8DT07PMNrKzs5Geno4OHTqI63R0dJQC5ovUq1cPtWvXhq2tLZ48eYKffvoJenp6r/R5tmzZAiMjI/Hr5MmTZe7XqFEj1KtXD7GxscjJycHFixfRpUsX2NjYwMHBAXFxceL1OiVB5urVq+jUqZNSO506dcLVq1eV1rVs2VL8vnbt2jAxMUFmZuYrfZ7n1a9fXww6AJCcnIzc3FxYWFgofebU1FTcuHFD3M/BwUEMOgDg7u4OhUKBlJSUl/aZlJQEbW1tdOnSpdx9IiMj0aZNG9StWxdGRkZYs2YN0tLSXthucnIyrl+/DmNjY7Fuc3Nz5OfnK9VORPS6aOzITnWaO3eu0l+sUlW7dm00atRIad0///yjtHz06FHs3bsXCxcuBPBs+lChUEBHRwdr1qxB3759q1TDyZMnYWJiAktLyypfGPvRRx8pha7nf8n/V9euXXHs2DG0bNkSjRs3FkNxyVSWIAho1KgR7O3tK1XDf+9kkslkUCgUlWqjLLVr11Zazs3NhY2NTZlv3a7qHWslSqYVy7Nt2zaMHz8eERERcHd3h7GxMRYsWID4+PgXHpebm4s2bdpgy5YtpbY9H+iIiF4XjQ07JcPid+/ehY2Njbj+7t27aN26tbjPf/+qfvr0KR4+fPjCIfzJkycjODhYXM7Jyan0Lz2piIuLU5ra2rNnD7799lucOXMGdnZ2MDU1hY2NDeLj48XRn6dPn4rXkLyMXC5X2S9nY2PjCgembt26YfTo0WjWrBm6du0qrvf09MTatWshCILS9JSzszNOnz4Nf///f4v/6dOn0axZs1euV09Pr9S0YUW5ubkhIyMDOjo6cHR0LHe/tLQ03LlzB7a2tgCAs2fPQktLq0JThS1atIBCocDx48eVpotLnD59Gh4eHkrXAP13ZKasz+jm5oYff/wRlpaW5U6NEhG9Tho7jSWXy2FtbY2YmBhxXU5ODuLj4+Hu7g7g2ZB9VlYWEhMTxX2OHj0KhUKhNALwX/r6+jAxMVH6elM5OzvDxcVF/LKzs4OWlhZcXFxQp04dAMCYMWMwb9487N69G3/88QeGDx+ukmfHZGRkICkpSbw769KlS0hKSirzgt/KKrluZ8OGDUrTNF26dEF8fDwSEhKUws6ECRMQFRWFlStX4tq1a1i0aBF27tz5wouKX8bR0RG5ubmIiYnB/fv3kZeXV+Fjvby84O7ujl69euGXX37BzZs3cebMGUydOhXnz58X9zMwMIC/vz+Sk5Nx8uRJjB49Gv3796/QdUKOjo7w9/fHkCFDsHv3bqSmpiI2Nla8ALlx48Y4f/48Dh8+jD///BOhoaE4d+5cqTZ+++03pKSk4P79+ygqKoKfnx/eeust9OzZEydPnhTbHT16dKmRRSKi10GtYSc3N1e8lgR4dlFyUlIS0tLSIJPJEBQUhFmzZmHv3r24dOkSPv/8c9ja2opPhHV2doaPjw+GDh2KhIQEnD59GiNHjsTAgQPFv3Sp6saNG4fPPvsM/v7+4nSGKi7+XrVqFVxdXTF06FAAz0ZdXF1dy72duzLkcjnq16+Px48fK4UdBwcH2NraorCwUGnEp1evXli6dCkWLlyI5s2bY/Xq1di4caPSPpXl4eGBYcOGYcCAAahbty7mz59f4WNlMhl+/vlneHp6YvDgwWjSpAkGDhyIv//+W+k6tkaNGqFPnz54//338e6776Jly5ZYsaJiD7wEnt1u//HHH2P48OFwcnLC0KFD8eTJswdhfvXVV+jTpw8GDBiADh064MGDB6Xu9Bo6dCiaNm2Ktm3bom7dujh9+jRq1aqFEydOwMHBAX369IGzszMCAwORn5//Rv9hQUTqIxMq+sCUavDfp+yW8Pf3R1RUFARBwPTp07FmzRpkZWWhc+fOWLFiBZo0aSLu+/DhQ4wcORL79u2DlpYW+vbti2XLlpW6C+lFcnJyYGpqiuzs7FI/jPPz85Gamqr0/JPKyMyr+sWrr8KyluXLd6IabcaMGdi9e7f4xwKpX1V/XhBR9VDrNTtdu3Z94cPpZDIZvvnmG3zzzTfl7mNubo6tW7dWR3lEREQkARp7zQ4RERGRKjDsENVQM2bM4BQWEVEFMOwQERGRpDHsVJAar+MmohqCPyeINBPDzktoa2sDqP6XVxJRzVfyLKX/PmmbiNRLY5+grCl0dHRQq1Yt3Lt3D7q6utDSqlw+LCooqqbKXixfK18t/RK9iQRBQF5eHjIzM2FmZib+kUREmoFh5yVkMhlsbGyQmpqKv//+u9LHPy58XA1VvVyOnjRfbkqkyczMzCr09Goier0YdipAT08PjRs3fqWprK1X1fMMoE/kn6ilX6I3la6uLkd0iDQUw04FaWlpvdITUfNl6plO4tNbiYiInuEFykRERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpOuougKrHiqQVaul3eOvhaumXiIioPBzZISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIknT6LBTXFyM0NBQyOVyGBoaomHDhpg5cyYEQRD3EQQBYWFhsLGxgaGhIby8vHDt2jU1Vk1ERESaRKPDzrfffouVK1fif//7H65evYpvv/0W8+fPx/Lly8V95s+fj2XLlmHVqlWIj49H7dq14e3tjfz8fDVWTkRERJpCo5+gfObMGfTs2RMffPABAMDR0RE//PADEhISADwb1VmyZAmmTZuGnj17AgA2b94MKysr7N69GwMHDlRb7URERKQZNHpkx8PDAzExMfjzzz8BAMnJyTh16hTee+89AEBqaioyMjLg5eUlHmNqaooOHTogLi6u3HYLCgqQk5Oj9EVERETSpNEjOyEhIcjJyYGTkxO0tbVRXFyM2bNnw8/PDwCQkZEBALCyslI6zsrKStxWlrlz5yI8PLz6CiciIiKNodEjO9u3b8eWLVuwdetWXLhwAZs2bcLChQuxadOmKrU7efJkZGdni1+3bt1SUcVERESkaTR6ZGfChAkICQkRr71p0aIF/v77b8ydOxf+/v6wtrYGANy9exc2NjbicXfv3kXr1q3LbVdfXx/6+vrVWjsRERFpBo0e2cnLy4OWlnKJ2traUCgUAAC5XA5ra2vExMSI23NychAfHw93d/fXWisRERFpJo0e2fH19cXs2bPh4OCA5s2b4+LFi1i0aBGGDBkCAJDJZAgKCsKsWbPQuHFjyOVyhIaGwtbWFr169VJv8URERKQRNDrsLF++HKGhoRg+fDgyMzNha2uLr776CmFhYeI+EydOxJMnT/Dll18iKysLnTt3xqFDh2BgYKDGyomIiEhTyITnH0f8hsrJyYGpqSmys7NhYmKi0rZXJK1QaXuabnjr4eougYiISIlGX7NDREREVFUMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpVQ47xcXFSEpKwqNHj1RRDxEREZFKVTrsBAUFYf369QCeBZ0uXbrAzc0N9vb2iI2NVXV9RERERFVS6bCzY8cOtGrVCgCwb98+pKam4o8//sDYsWMxdepUlRdIREREVBWVDjv379+HtbU1AODnn39Gv3790KRJEwwZMgSXLl1SeYFEREREVVHpsGNlZYUrV66guLgYhw4dwjvvvAMAyMvLg7a2tsoLJCIiIqoKncoeMHjwYPTv3x82NjaQyWTw8vICAMTHx8PJyUnlBRIRERFVRaXDzowZM+Di4oJbt26hX79+0NfXBwBoa2sjJCRE5QUSERERVUWlw87mzZsxYMAAMeSUGDRoELZt26aywoiIiIhUodLX7AwePBjZ2dml1j9+/BiDBw9WSVFEREREqlLpsCMIAmQyWan1//zzD0xNTVVSFBEREZGqVHgay9XVFTKZDDKZDD169ICOzv8/tLi4GKmpqfDx8amWIomIiIheVYXDTq9evQAASUlJ8Pb2hpGRkbhNT08Pjo6O6Nu3r8oLJCIiIqqKCoed6dOnAwAcHR0xYMAAGBgYVFtRRERERKpS6bux/P39AQCFhYXIzMyEQqFQ2u7g4KCayoiIiIhUoNJh59q1axgyZAjOnDmjtL7kwuXi4mKVFUdERERUVZUOOwEBAdDR0cH+/fvFpygTERERaapKh52kpCQkJiby1RBERERUI1T6OTvNmjXD/fv3q6MWIiIiIpWrdNj59ttvMXHiRMTGxuLBgwfIyclR+iIiIiLSJJWexip5y3mPHj2U1vMCZSIiFTo2Vz39dpusnn6JqlGlw86xY8eqow4iIiKialHpsNOlS5fqqIOIiIioWlT6mh0AOHnyJD799FN4eHjg9u3bAIDvvvsOp06dUmlxRERERFVV6bDz008/wdvbG4aGhrhw4QIKCgoAANnZ2ZgzZ47KCyQiIiKqikqHnVmzZmHVqlVYu3YtdHV1xfWdOnXChQsXVFocERERUVVVOuykpKTA09Oz1HpTU1NkZWWpoiYiIiIilan0BcrW1ta4fv06HB0dldafOnUKDRo0UFVdkhF344Fa+nVvaKGWfomIiDRNpUd2hg4dijFjxiA+Ph4ymQx37tzBli1bMH78eHz99dfVUSMRERHRK6v0yE5ISAgUCgV69OiBvLw8eHp6Ql9fH+PHj8eoUaOqo0YiIiKiV1bpsCOTyTB16lRMmDAB169fR25uLpo1awYjI6PqqI+IiIioSioddkro6emhWbNmqqyFiIiISOUqFHb69OlT4QZ37tz5ysUQERERqVqFwo6pqan4vSAI2LVrF0xNTdG2bVsAQGJiIrKysioViohUafGRP9XS79h3mqilXyIiqrgKhZ2NGzeK30+aNAn9+/fHqlWroK2tDQAoLi7G8OHDYWJiUj1VEhEREb2iSt96vmHDBowfP14MOgCgra2N4OBgbNiwQaXFAcDt27fx6aefwsLCAoaGhmjRogXOnz8vbhcEAWFhYbCxsYGhoSG8vLxw7do1lddBRERENVOlw87Tp0/xxx9/lFr/xx9/QKFQqKSoEo8ePUKnTp2gq6uLgwcP4sqVK4iIiECdOnXEfebPn49ly5Zh1apViI+PR+3ateHt7Y38/HyV1kJEREQ1U6Xvxho8eDACAwNx48YNtG/fHgAQHx+PefPmYfDgwSot7ttvv4W9vb3SNJpcLhe/FwQBS5YswbRp09CzZ08AwObNm2FlZYXdu3dj4MCBKq2HiIiIap5Kh52FCxfC2toaERERSE9PBwDY2NhgwoQJGDdunEqL27t3L7y9vdGvXz8cP34cdnZ2GD58OIYOHQoASE1NRUZGBry8vMRjTE1N0aFDB8TFxZUbdgoKCsS3tQNATk6OSusmIiIizVHpaSwtLS1MnDgRt2/fRlZWFrKysnD79m1MnDhR6ToeVfjrr7+wcuVKNG7cGIcPH8bXX3+N0aNHY9OmTQCAjIwMAICVlZXScVZWVuK2ssydOxempqbil729vUrrJiIiIs1R6bDzPBMTk2q9A0uhUMDNzQ1z5syBq6srvvzySwwdOhSrVq2qUruTJ09Gdna2+HXr1i0VVUxERESaptLTWHK5HDKZrNztf/31V5UKep6NjU2ppzQ7Ozvjp59+AvDsDewAcPfuXdjY2Ij73L17F61bty63XX19fejr66usTiIiItJclQ47QUFBSstFRUW4ePEiDh06hAkTJqiqLgBAp06dkJKSorTuzz//RP369QE8C17W1taIiYkRw01OTg7i4+P5BnYiIiIC8AphZ8yYMWWuj4yMVHr+jSqMHTsWHh4emDNnDvr374+EhASsWbMGa9asAfDspaRBQUGYNWsWGjduDLlcjtDQUNja2qJXr14qrYWIiIhqpipds/O89957T5xeUpV27dph165d+OGHH+Di4oKZM2diyZIl8PPzE/eZOHEiRo0ahS+//BLt2rVDbm4uDh06BAMDA5XWQkRERDXTK7/1/L927NgBc3NzVTUn+vDDD/Hhhx+Wu10mk+Gbb77BN998o/K+iYiIqOardNhxdXVVukBZEARkZGTg3r17WLFihUqLIyIiIqqqSoednj17KoUdLS0t1K1bF127doWTk5NKiyMiIiKqqkqHnRkzZlRDGURERETVo9IXKGtrayMzM7PU+gcPHqj8CcpEREREVVXpsCMIQpnrCwoKoKenV+WCiIiIiFSpwtNYy5YtA/Ds7qd169bByMhI3FZcXIwTJ07wmh0iIiLSOBUOO4sXLwbwbGRn1apVSlNWenp6cHR0rPI7q4iIiIhUrcJhJzU1FQDQrVs37Ny5E3Xq1Km2ooiIiIhUpdJ3Yx07dkxp+enTp8jPz1ea1iIiIiLSFBW+QHnfvn2IiopSWjd79mwYGRnBzMwM7777Lh49eqTq+oiIiIiqpMJhZ9GiRXjy5Im4fObMGYSFhSE0NBTbt2/HrVu3MHPmzGopkoiIiOhVVTjs/P777/Dw8BCXd+zYgXfeeQdTp05Fnz59EBERgX379lVLkURERESvqsJh5/Hjx7CwsBCXT506hR49eojLzZs3x507d1RbHREREVEVVTjs2NnZ4erVqwCA3NxcJCcnK430PHjwALVq1VJ9hURERERVUOGw069fPwQFBeG7777D0KFDYW1tjY4dO4rbz58/j6ZNm1ZLkURERESvqsK3noeFheH27dsYPXo0rK2t8f333ys9WPCHH36Ar69vtRRJRERE9KoqHHYMDQ2xefPmcrf/9/k7RERERJqg0i8CJSIiIqpJGHaIiIhI0hh2iIiISNIYdoiIiEjSGHaIiIhI0l4p7IwcORIPHz5UdS1EREREKlfhsPPPP/+I32/duhW5ubkAgBYtWuDWrVuqr4yIiIhIBSr8nB0nJydYWFigU6dOyM/Px61bt+Dg4ICbN2+iqKioOmskIiIiemUVHtnJyspCdHQ02rRpA4VCgffffx9NmjRBQUEBDh8+jLt371ZnnURERESvpMJhp6ioCO3bt8e4ceNgaGiIixcvYuPGjdDW1saGDRsgl8v5biwiIiLSOBWexjIzM0Pr1q3RqVMnFBYW4t9//0WnTp2go6ODH3/8EXZ2djh37lx11kpUrgs5P6qp51A19UtERBVV4ZGd27dvY9q0adDX18fTp0/Rpk0bvP322ygsLMSFCxcgk8nQuXPn6qyViIiIqNIqHHbeeust+Pr6Yu7cuahVqxbOnTuHUaNGQSaTYfz48TA1NUWXLl2qs1YiIiKiSnvlhwqampqif//+0NXVxdGjR5Gamorhw4ersjYiIiKiKqvwNTvP++2332BnZwcAqF+/PnR1dWFtbY0BAwaotDgiIiKiqnqlsGNvby9+f/nyZZUVQ0RERKRqrxR2iIheu2Nz1dNvt8nq6ZeIVIYvAiUiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSdNRdwFENdniI3+qre+x7zRRW98kYcfmqqffbpPV0y+9ETiyQ0RERJJWo8LOvHnzIJPJEBQUJK7Lz8/HiBEjYGFhASMjI/Tt2xd3795VX5FERESkUWpM2Dl37hxWr16Nli1bKq0fO3Ys9u3bh+joaBw/fhx37txBnz591FQlERERaZoaEXZyc3Ph5+eHtWvXok6dOuL67OxsrF+/HosWLUL37t3Rpk0bbNy4EWfOnMHZs2fVWDERERFpihoRdkaMGIEPPvgAXl5eSusTExNRVFSktN7JyQkODg6Ii4t73WUSERGRBtL4u7G2bduGCxcu4Ny5c6W2ZWRkQE9PD2ZmZkrrrayskJGRUW6bBQUFKCgoEJdzcnJUVi8RERFpFo0OO7du3cKYMWNw5MgRGBgYqKzduXPnIjw8XGXtaaK4Gw/U0u/w1mrploiIqFwaPY2VmJiIzMxMuLm5QUdHBzo6Ojh+/DiWLVsGHR0dWFlZobCwEFlZWUrH3b17F9bW1uW2O3nyZGRnZ4tft27dquZPQkREROqi0SM7PXr0wKVLl5TWDR48GE5OTpg0aRLs7e2hq6uLmJgY9O3bFwCQkpKCtLQ0uLu7l9uuvr4+9PX1q7V2IiIi0gwaHXaMjY3h4uKitK527dqwsLAQ1wcGBiI4OBjm5uYwMTHBqFGj4O7ujo4dO6qjZCIiItIwGh12KmLx4sXQ0tJC3759UVBQAG9vb6xYsULdZREREZGGqHFhJzY2VmnZwMAAkZGRiIyMVE9BREREpNE0+gJlIiIioqpi2CEiIiJJY9ghIiIiSatx1+wQERGpzLG56um322T19PuG4sgOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUmajroLIKrJLuT8qMbeQ9XYNxFRzcGRHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjQddRdA0rIiaYW6S6DqdmyuWrqN++uBWvp176aWbolIhTiyQ0RERJKm0WFn7ty5aNeuHYyNjWFpaYlevXohJSVFaZ/8/HyMGDECFhYWMDIyQt++fXH37l01VUxERESaRqPDzvHjxzFixAicPXsWR44cQVFREd599108efJE3Gfs2LHYt28foqOjcfz4cdy5cwd9+vRRY9VERESkSTT6mp1Dhw4pLUdFRcHS0hKJiYnw9PREdnY21q9fj61bt6J79+4AgI0bN8LZ2Rlnz55Fx44d1VE2ERERaRCNHtn5r+zsbACAubk5ACAxMRFFRUXw8vIS93FycoKDgwPi4uLKbaegoAA5OTlKX0RERCRNGj2y8zyFQoGgoCB06tQJLi4uAICMjAzo6enBzMxMaV8rKytkZGSU29bcuXMRHh5eneUSSZa67ooiInpVNWZkZ8SIEbh8+TK2bdtW5bYmT56M7Oxs8evWrVsqqJCIiIg0UY0Y2Rk5ciT279+PEydOoF69euJ6a2trFBYWIisrS2l05+7du7C2ti63PX19fejr61dnyURERKQhNDrsCIKAUaNGYdeuXYiNjYVcLlfa3qZNG+jq6iImJgZ9+/YFAKSkpCAtLQ3u7u7qKJmIiOjl1PRwTnSbrJ5+1Uyjw86IESOwdetW7NmzB8bGxuJ1OKampjA0NISpqSkCAwMRHBwMc3NzmJiYYNSoUXB3d+edWERERARAw8POypUrAQBdu3ZVWr9x40YEBAQAABYvXgwtLS307dsXBQUF8Pb2xooVfGUBERERPaPRYUcQhJfuY2BggMjISERGRr6GioiIiKimqTF3YxERERG9CoYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSNvhuLSNPVy0lUdwlERPQSHNkhIiIiSWPYISIiIklj2CEiIiJJ4zU7RESkfup6MSa9ETiyQ0RERJLGsENERESSxmksUqm4Gw/U0i9vASepiftLPf8vuTewUEu/RNWJIztEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGm8G4uIiES8C0zi1PXwxm6T1dPv/+HIDhEREUkaww4RERFJGqexiKhS9mpdV0u/HykaqaVfIqr5OLJDREREksawQ0RERJLGaSyiGmrxkT/VXQIRUY3AkR0iIiKSNIYdIiIikjROYxER0RuLD1F8M3Bkh4iIiCSNYYeIiIgkjdNYREQvoq53CRGRynBkh4iIiCSNYYeIiIgkjdNY1axeTqJa+v3HpI1a+qXXp2PaGrX0u5d/IhFRDcMfW0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkabwbi4iI6A2htneBdVNLtyKO7BAREZGkMewQERGRpHEai1RKXQ9RfBPt1bqu7hKIiGoEjuwQERGRpDHsEBERkaRxGkuiOJ1ERET0DEd2iIiISNIYdoiIiEjSGHaIiIhI0njNDhHVCOq61d4dFmrpV12f9yNFI7X0q64n+6rLm/Z51U0yIzuRkZFwdHSEgYEBOnTogISEBHWXRERERBpAEmHnxx9/RHBwMKZPn44LFy6gVatW8Pb2RmZmprpLIyIiIjWTxDTWokWLMHToUAwePBgAsGrVKhw4cAAbNmxASEiImqsjoppMbdMNkvhTlEgz1Pj/nQoLC5GYmAgvLy9xnZaWFry8vBAXF6fGyoiIiEgT1PiRnfv376O4uBhWVlZK662srPDHH3+UeUxBQQEKCgrE5ezsbABATk6Oyusr+LdI5W0S0evzRFHw8p2qQYGWen52qOvzkrRVx+/X5xkbG0Mmk5W7vcaHnVcxd+5chIeHl1pvb2+vhmqISJMtVncBr9mb9nnpNRn1v2ptPjs7GyYmJuVur/Fh56233oK2tjbu3r2rtP7u3buwtrYu85jJkycjODhYXFYoFHj48CEsLCxemAwrKycnB/b29rh169YL/xGoanieXx+e69eD5/n14Hl+PV7HeTY2Nn7h9hofdvT09NCmTRvExMSgV69eAJ6Fl5iYGIwcObLMY/T19aGvr6+0zszMrNpqNDEx4f9IrwHP8+vDc/168Dy/HjzPr4c6z3ONDzsAEBwcDH9/f7Rt2xbt27fHkiVL8OTJE/HuLCIiInpzSSLsDBgwAPfu3UNYWBgyMjLQunVrHDp0qNRFy0RERPTmkUTYAYCRI0eWO22lLvr6+pg+fXqpKTNSLZ7n14fn+vXgeX49eJ5fD004zzJBEAS19U5ERERUzWr8QwWJiIiIXoRhh4iIiCSNYYeIiIgkjWGHiIiIJI1hpxpFRkbC0dERBgYG6NChAxISEtRdUo02d+5ctGvXDsbGxrC0tESvXr2QkpKitE9+fj5GjBgBCwsLGBkZoW/fvqWerk0VN2/ePMhkMgQFBYnreI5V5/bt2/j0009hYWEBQ0NDtGjRAufPnxe3C4KAsLAw2NjYwNDQEF5eXrh27ZoaK655iouLERoaCrlcDkNDQzRs2BAzZ87E8/fm8DxX3okTJ+Dr6wtbW1vIZDLs3r1baXtFzunDhw/h5+cHExMTmJmZITAwELm5udVTsEDVYtu2bYKenp6wYcMG4ffffxeGDh0qmJmZCXfv3lV3aTWWt7e3sHHjRuHy5ctCUlKS8P777wsODg5Cbm6uuM+wYcMEe3t7ISYmRjh//rzQsWNHwcPDQ41V11wJCQmCo6Oj0LJlS2HMmDHiep5j1Xj48KFQv359ISAgQIiPjxf++usv4fDhw8L169fFfebNmyeYmpoKu3fvFpKTk4WPPvpIkMvlwr///qvGymuW2bNnCxYWFsL+/fuF1NRUITo6WjAyMhKWLl0q7sPzXHk///yzMHXqVGHnzp0CAGHXrl1K2ytyTn18fIRWrVoJZ8+eFU6ePCk0atRIGDRoULXUy7BTTdq3by+MGDFCXC4uLhZsbW2FuXPnqrEqacnMzBQACMePHxcEQRCysrIEXV1dITo6Wtzn6tWrAgAhLi5OXWXWSI8fPxYaN24sHDlyROjSpYsYdniOVWfSpElC586dy92uUCgEa2trYcGCBeK6rKwsQV9fX/jhhx9eR4mS8MEHHwhDhgxRWtenTx/Bz89PEASeZ1X4b9ipyDm9cuWKAEA4d+6cuM/BgwcFmUwm3L59W+U1chqrGhQWFiIxMRFeXl7iOi0tLXh5eSEuLk6NlUlLdnY2AMDc3BwAkJiYiKKiIqXz7uTkBAcHB573ShoxYgQ++OADpXMJ8Byr0t69e9G2bVv069cPlpaWcHV1xdq1a8XtqampyMjIUDrXpqam6NChA891JXh4eCAmJgZ//vknACA5ORmnTp3Ce++9B4DnuTpU5JzGxcXBzMwMbdu2Fffx8vKClpYW4uPjVV6TZJ6grEnu37+P4uLiUq+rsLKywh9//KGmqqRFoVAgKCgInTp1gouLCwAgIyMDenp6pV7qamVlhYyMDDVUWTNt27YNFy5cwLlz50pt4zlWnb/++gsrV65EcHAwpkyZgnPnzmH06NHQ09ODv7+/eD7L+jnCc11xISEhyMnJgZOTE7S1tVFcXIzZs2fDz88PAHieq0FFzmlGRgYsLS2Vtuvo6MDc3LxazjvDDtVII0aMwOXLl3Hq1Cl1lyIpt27dwpgxY3DkyBEYGBiouxxJUygUaNu2LebMmQMAcHV1xeXLl7Fq1Sr4+/uruTrp2L59O7Zs2YKtW7eiefPmSEpKQlBQEGxtbXme3yCcxqoGb731FrS1tUvdoXL37l1YW1urqSrpGDlyJPbv349jx46hXr164npra2sUFhYiKytLaX+e94pLTExEZmYm3NzcoKOjAx0dHRw/fhzLli2Djo4OrKyseI5VxMbGBs2aNVNa5+zsjLS0NAAQzyd/jlTNhAkTEBISgoEDB6JFixb47LPPMHbsWMydOxcAz3N1qMg5tba2RmZmptL2p0+f4uHDh9Vy3hl2qoGenh7atGmDmJgYcZ1CoUBMTAzc3d3VWFnNJggCRo4ciV27duHo0aOQy+VK29u0aQNdXV2l856SkoK0tDSe9wrq0aMHLl26hKSkJPGrbdu28PPzE7/nOVaNTp06lXp0wp9//on69esDAORyOaytrZXOdU5ODuLj43muKyEvLw9aWsq/6rS1taFQKADwPFeHipxTd3d3ZGVlITExUdzn6NGjUCgU6NChg+qLUvklzyQIwrNbz/X19YWoqCjhypUrwpdffimYmZkJGRkZ6i6txvr6668FU1NTITY2VkhPTxe/8vLyxH2GDRsmODg4CEePHhXOnz8vuLu7C+7u7mqsuuZ7/m4sQeA5VpWEhARBR0dHmD17tnDt2jVhy5YtQq1atYTvv/9e3GfevHmCmZmZsGfPHuG3334TevbsyVuiK8nf31+ws7MTbz3fuXOn8NZbbwkTJ04U9+F5rrzHjx8LFy9eFC5evCgAEBYtWiRcvHhR+PvvvwVBqNg59fHxEVxdXYX4+Hjh1KlTQuPGjXnreU20fPlywcHBQdDT0xPat28vnD17Vt0l1WgAyvzauHGjuM+///4rDB8+XKhTp45Qq1YtoXfv3kJ6err6ipaA/4YdnmPV2bdvn+Di4iLo6+sLTk5Owpo1a5S2KxQKITQ0VLCyshL09fWFHj16CCkpKWqqtmbKyckRxowZIzg4OAgGBgZCgwYNhKlTpwoFBQXiPjzPlXfs2LEyfx77+/sLglCxc/rgwQNh0KBBgpGRkWBiYiIMHjxYePz4cbXUKxOE5x4jSURERCQxvGaHiIiIJI1hh4iIiCSNYYeIiIgkjWGHiIiIJI1hh4iIiCSNYYeIiIgkjWGHiIiIJI1hh4hqBEEQ8OWXX8Lc3BwymQxJSUnqLomIagiGHSKqki1btsDe3h516tRBcHCw0rabN2+iSZMmyMnJqXI/hw4dQlRUFPbv34/09HS4uLhUuU0iejPoqLsAIqq57t+/jy+++AJRUVFo0KABPvjgA3Tv3h0ffvghAGD48OGYN28eTExMqtzXjRs3YGNjAw8Pjyq3pQ6FhYXQ09NTdxlEbySO7BDRK/vrr79gamqKAQMGoF27dujWrRuuXr0KAPjhhx+gq6uLPn36VKit48ePo3379tDX14eNjQ1CQkLw9OlTAEBAQABGjRqFtLQ0yGQyODo6ltlGVFQUzMzMsHv3bjRu3BgGBgbw9vbGrVu3xH1u3LiBnj17wsrKCkZGRmjXrh1+/fVXpXYcHR0xc+ZMDBo0CLVr14adnR0iIyOV9snKysIXX3yBunXrwsTEBN27d0dycrK4fcaMGWjdujXWrVsHuVwOAwMDAMCOHTvQokULGBoawsLCAl5eXnjy5EmFzhERvRqGHSJ6ZY0bN0ZeXh4uXryIhw8f4ty5c2jZsiUePXqE0NBQ/O9//6tQO7dv38b777+Pdu3aITk5GStXrsT69esxa9YsAMDSpUvxzTffoF69ekhPT8e5c+fKbSsvLw+zZ8/G5s2bcfr0aWRlZWHgwIHi9tzcXLz//vuIiYnBxYsX4ePjA19fX6SlpSm1s2DBArRq1QoXL15ESEgIxowZgyNHjojb+/Xrh8zMTBw8eBCJiYlwc3NDjx498PDhQ3Gf69ev46effsLOnTuRlJSE9PR0DBo0CEOGDMHVq1cRGxuLPn36gK8oJKpm1fJ6USJ6Y+zcuVNwcXERGjZsKEyfPl0QBEEYMmSIsHjxYuH48eNC69athebNmwvR0dHltjFlyhShadOmgkKhENdFRkYKRkZGQnFxsSAIgrB48WKhfv36L6xl48aNAgDh7Nmz4rqrV68KAIT4+Phyj2vevLmwfPlycbl+/fqCj4+P0j4DBgwQ3nvvPUEQBOHkyZOCiYmJkJ+fr7RPw4YNhdWrVwuCIAjTp08XdHV1hczMTHF7YmKiAEC4efPmCz8HEakWr9khoirp3bs3evfuLS4fP34cv/32G5YvX45GjRrhhx9+gLW1Ndq3bw9PT09YWlqWauPq1atwd3eHTCYT13Xq1Am5ubn4559/4ODgUOF6dHR00K5dO3HZyckJZmZmuHr1Ktq3b4/c3FzMmDEDBw4cQHp6Op4+fYp///231MiOu7t7qeUlS5YAAJKTk5GbmwsLCwulff7991/cuHFDXK5fvz7q1q0rLrdq1Qo9evRAixYt4O3tjXfffRcff/wx6tSpU+HPR0SVx7BDRCpTUFCA4cOH47vvvsP169fx9OlTdOnSBQDQpEkTxMfHw9fXV601jh8/HkeOHMHChQvRqFEjGBoa4uOPP0ZhYWGF28jNzYWNjQ1iY2NLbTMzMxO/r127ttI2bW1tHDlyBGfOnMEvv/yC5cuXY+rUqYiPj4dcLn/Vj0REL8FrdohIZWbNmgUfHx+4ubmhuLhYvMAYAIqKilBcXFzmcc7OzoiLi1O6duX06dMwNjZGvXr1KlXD06dPcf78eXE5JSUFWVlZcHZ2FtsNCAhA79690aJFC1hbW+PmzZul2jl79myp5ZI23NzckJGRAR0dHTRq1Ejp66233nphfTKZDJ06dUJ4eDguXrwIPT097Nq1q1KfkYgqhyM7RKQSV65cwY8//oiLFy8CeDZ9pKWlhfXr18Pa2hp//PGH0vTS84YPH44lS5Zg1KhRGDlyJFJSUjB9+nQEBwdDS6tyf5Pp6upi1KhRWLZsGXR0dDBy5Eh07NgR7du3B/DsouqdO3fC19cXMpkMoaGhUCgUpdo5ffo05s+fj169euHIkSOIjo7GgQMHAABeXl5wd3dHr169MH/+fDRp0gR37tzBgQMH0Lt3b7Rt27bM2uLj4xETE4N3330XlpaWiI+Px71798QQRUTVg2GHiKpM+L+nGy9atEicujE0NERUVBRGjBiBgoIC/O9//4OdnV2Zx9vZ2eHnn3/GhAkT0KpVK5ibmyMwMBDTpk2rdC21atXCpEmT8Mknn+D27dt4++23sX79enH7okWLMGTIEHh4eOCtt97CpEmTynzo4bhx43D+/HmEh4fDxMQEixYtgre3N4BnozM///wzpk6disGDB+PevXuwtraGp6cnrKysyq3NxMQEJ06cwJIlS5CTk4P69esjIiIC7733XqU/JxFVnEwQeM8jEUlDVFQUgoKCkJWVVaV2HB0dERQUhKCgIJXURUTqxWt2iIiISNIYdoiIiEjSOI1FREREksaRHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikrT/B7mZ7WNFYNtzAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'H4 - P2')"
            ]
          },
          "metadata": {},
          "execution_count": 110
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H4_P1_weighted_sum = H4d_a_P1*1+H4d_b_P1*2+H4d_c_P1*3\n",
        "H4_P2_weighted_sum = H4d_a_P2*1+H4d_b_P2*2+H4d_c_P2*3\n",
        "\n",
        "print(\"Pre-test average\", H4_P1_weighted_sum.mean())\n",
        "print(\"Post-test average\", H4_P2_weighted_sum.mean())\n",
        "\n",
        "print(stats.ttest_rel(H4_P1_weighted_sum, H4_P2_weighted_sum))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQbYBEqqC8W9",
        "outputId": "25b81b27-90e5-414e-9db0-f6d403c6a97f"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pre-test average 185.25078369905955\n",
            "Post-test average 180.91692789968653\n",
            "TtestResult(statistic=1.7541574071624162, pvalue=0.08036676460336742, df=318)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H4d_a_P1 = P1['a) The analysis will replicate exactly (the replication attempt will produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures) .2']\n",
        "H4d_b_P1 = P1['b) The analysis will replicate qualitatively (the replication attempt will produce results that have small differences with the paper, but these will still agree with the abstract-level findings of the paper).2']\n",
        "H4d_c_P1 = P1['c) The analysis won’t  replicate at all (the replication attempt will produce results that are in conflict with the abstract-level findings of the paper) .2']\n",
        "\n",
        "H4d_a_P2 = P2['a) The analysis will replicate exactly (the replication attempt will produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures) .2']\n",
        "H4d_b_P2 = P2['b) The analysis will replicate qualitatively (the replication attempt will produce results that have small differences with the paper, but these will still agree with the abstract-level findings of the paper).2']\n",
        "H4d_c_P2 = P2['c) The analysis won’t  replicate at all (the replication attempt will produce results that are in conflict with the abstract-level findings of the paper) .2']"
      ],
      "metadata": {
        "id": "Gt5kHpbuDXd3"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "P1['H4_estimation_sum'] = H4d_a_P1+H4d_b_P1+H4d_c_P1\n",
        "P4['H4_estimation_sum'] = H4d_a_P2+H4d_b_P2+H4d_c_P2\n",
        "\n",
        "students_with_mistakes = set(P1[P1['H4_estimation_sum']!=100].UID.to_list()\\\n",
        "                                +P4[P4['H4_estimation_sum']!=100].UID.to_list())\n",
        "\n",
        "print(\"Total students to discard: {}\".format(len(students_with_mistakes)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rz0RqFTDXCtZ",
        "outputId": "6a910258-fb0e-4dd8-bc5b-b8e515272962"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total students to discard: 16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H4_clean_data = P1[P1.UID.apply(lambda r: r not in students_with_mistakes)]\\\n",
        "                    .merge(P4, on=\"UID\")\n",
        "\n",
        "\n",
        "H4d_a_P1 = H4_clean_data['a) The analysis will replicate exactly (the replication attempt will produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures) .1_x']\n",
        "H4d_b_P1 = H4_clean_data['b) The analysis will replicate qualitatively (the replication attempt will produce results that have small differences with the paper, but these will still agree with the abstract-level findings of the paper).1_x']\n",
        "H4d_c_P1 = H4_clean_data['c) The analysis won’t  replicate at all (the replication attempt will produce results that are in conflict with the abstract-level findings of the paper) .1_x']\n",
        "\n",
        "H4d_a_P2 = H4_clean_data['a) The analysis will replicate exactly (the replication attempt will produce results that agree exactly with the paper,  up to the decimals printed in the paper or shown in the figures) .1_y']\n",
        "H4d_b_P2 = H4_clean_data['b) The analysis will replicate qualitatively (the replication attempt will produce results that have small differences with the paper, but these will still agree with the abstract-level findings of the paper).1_y']\n",
        "H4d_c_P2 = H4_clean_data['c) The analysis won’t  replicate at all (the replication attempt will produce results that are in conflict with the abstract-level findings of the paper) .1_y']"
      ],
      "metadata": {
        "id": "X0JOIMPyXAC4"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "H4_P1_weighted_sum = H4d_a_P1*1+H4d_b_P1*2+H4d_c_P1*3\n",
        "H4_P2_weighted_sum = H4d_a_P2*1+H4d_b_P2*2+H4d_c_P2*3\n",
        "\n",
        "print(\"Pre-test average\", H4_P1_weighted_sum.mean())\n",
        "print(\"Post-test average\", H4_P2_weighted_sum.mean())\n",
        "\n",
        "print(stats.ttest_rel(H4_P1_weighted_sum, H4_P2_weighted_sum))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wDYPam9jXOgf",
        "outputId": "9fdb071b-1766-4786-f75c-3dcbd0da1e41"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pre-test average 185.67092651757187\n",
            "Post-test average 181.39776357827475\n",
            "TtestResult(statistic=1.7328493413782806, pvalue=0.08411055421114333, df=312)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dwxJc700XSRK"
      },
      "execution_count": 115,
      "outputs": []
    }
  ]
}